Semantic Web Recommender Systems

Transcription

Cai-Nicolas Ziegler
Institut für Informatik, Group DBIS,
Universität Freiburg, Germany
cziegler@informatik.uni-freiburg.de
Abstract. Research on recommender systems has primarily addressed centralized
scenarios and largely ignored open, decentralized systems where remote information distribution prevails. The absence of superordinate authorities having full
access and control introduces some serious issues requiring novel approaches and
methods. Hence, our primary objective targets the successful deployment and integration of recommender system facilities for Semantic Web applications, making
use of novel technologies and concepts and incorporating them into one coherent
framework.
1 Introduction
Automated recommender systems intend to provide people with recommendations
of products they might appreciate, taking into account their past product ratings profile
and history of purchase or interest. Most successful systems apply so-called social
filtering techniques [9], dubbed collaborative filtering [6]. These systems identify
similar users and make recommendations based upon products people utterly
fancy.
Unfortunately, common collaborative filtering methods fail when transplanted into
decentralized scenarios. Analyzing the issues specific to these domains, we believe that
two novel approaches may alleviate the prevailing problems, namely trust networks,
along with trust propagation mechanisms, and taxonomy-driven profile generation and
filtering. One aspect of our work hence addresses the conception of suitable components,
specifically tailored to suit our decentralized setting, while another regards the seamless
integration of these latter building bricks into one single, unified framework. Empirical
analysis and performance evaluations are conducted at all stages.
2 Research Issues
Deploying recommender systems into the Semantic Web implies diverse, multi-faceted
issues, some of them being inherent to decentralized systems in general, others being
specific. Hereby, our devised Semantic Web recommender system performs all recommendation computations locally for one given user. Its principal difference from generic,
centralized approaches refers to information storage, supposing all user and rating data
distributed throughout the Semantic Web. Hence its decentralized nature. We thus come
to identify several research issues:
W. Lindner et al. (Eds.): EDBT 2004 Workshops, LNCS 3268, pp. 78–89, 2004.
c Springer-Verlag Berlin Heidelberg 2004
79
– Ontological Commitment. Basically, the Semantic Web is made up of machinereadable content distributed all over the Web. In order to ensure that agents can
understand and reason about the respective information, semantic interoperability
via ontologies or common content models must be established. For instance, FOAF
[5], an acronym for “Friend of a Friend”, defines an ontology for establishing simple
social networks and represents an open standard agents can rely upon.
– Interaction Facilities. Decentralized recommender systems have primarily been
subject to multi-agent research projects. In suchlike settings, environment models
are agent-centric, enabling agents to directly communicate with their peers and thus
making synchronous message exchange feasible. The Semantic Web, being an aggregation of distributed metadata, constitutes an inherently data-centric environment
model. Messages are exchanged by publishing or updating documents encoded in
RDF, OWL, or similar formats. Hence, the communication becomes restricted to
asynchronous message exchange only.
– Security and Credibility. Closed communities generally possess efficient means to
control the users’ identity and penalize malevolent behavior. Decentralized systems,
among those peer-to-peer networks, open marketplaces and the Semantic Web, likewise, cannot prevent deception and insincerity. Spoofing and identity forging thus
become facile to achieve [22]. Hence, some subjective means enabling each individual to decide which peers and content to rely upon are needed.
– Computational Complexity and Scalability. Centralized systems allow for estimating and limiting the community size and may thus tailor their filtering systems to ensure scalability. Note that user similarity assessment, which is an integral
part of collaborative filtering [6], implies some computation-intensive processes.
The Semantic Web will once contain millions of machine-readable homepages.
Computing similarity measures for all these “individuals” thus becomes infeasible.
Consequently, scalability can only be ensured when restricting these computations
to sufficiently narrow neighborhoods. Intelligent filtering mechanisms are needed,
still ensuring reasonable recall, i.e., not sacrificing too many relevant, like-minded
agents.
– Low Profile Overlap. Interest profiles are generally represented by vectors indicating the user’s opinion for every product. In order to reduce dimensionality and ensure
profile overlap, some centralized systems like Ringo [20] require users to rate small
subsets of the overall product space. These mandatory assessments, provisional tools
for creating overlap-ensuring profiles, imply additional efforts for prospective users.
Other recommenders, among those GroupLens and MovieLens [14], operate in domains where product sets are comparatively small. On the Semantic Web, virtually
no restrictions can be imposed on agents regarding which items to rate. Hence, new
approaches to ensure profile overlap are needed in order to make profile similarity
measures meaningful.
3 Proposed Approach
Endeavors to ensure semantical interoperability through ontologies constitute the cornerstone of Semantic Web conception and have been subject to numerous research projects.
80
C.-N. Ziegler
We do not concentrate our efforts on this aspect but suppose data compatibility from
the outset. Our interest rather focuses on handling computational complexity, security,
data-centric message passing, and profile vector overlap. Hereby, our approach builds
upon two fundamental notions, namely taxonomy-driven interest profile assembly and
trust networks. The exploitation of synergies of both intrinsically separate concepts helps
us leverage recommender system facilities into the Semantic Web.
3.1
Information Model
The infrastructure of the Semantic Web defines interlinked XML-documents made up
of machine-readable metadata. Our information model presented below well complies
with its design goals and allows facile mapping into RDF, OWL, etc.:
– Set of agents A = {a1 , a2 , . . . , an }. Set A contains all agents part of the community.
Globally unique identifiers are assigned through URIs.
– Set of products B = {b1 , b2 , . . . , bm }. All products considered are comprised in
set B. Hereby, unique identifiers may refer to product descriptions from an online
shop agreed upon, such as Amazon.com, or globally accepted codes, like ISBNs in
case of books.
– Set of partial trust functions T = {t1 , t2 , . . . , tn }. Every agent ai ∈ A has one
partial trust function ti : A → [−1, +1]⊥ that assigns continuous trust values to its
peers. Functions ti ∈ A are partial since agents generally only rate small subsets of
the overall community, hence rendering ti sparse:
p, if trust(ai , aj ) = p
ti (aj ) =
(1)
⊥, if no trust statement for aj from ai
We define high values for ti (aj ) to denote high trust from ai in aj , and negative
values to express distrust, respectively. Values around zero indicate the absence of
trust, not to be confused with explicit distrust [11].
– Set of partial rating functions R = {r1 , r2 , . . . , rn }. In addition to functions
ti ∈ T , every ai ∈ A has one partial function ri : B → [−1, +1]⊥ that expresses
his liking or dislike of product bk ∈ B. No person can rate every available product,
so functions ri ∈ B are necessarily partial.
p, if rates(ai , bk ) = p
ri (bk ) =
(2)
⊥, if no rating for bk from ai
Intuitively, high positive values for ri (bk ) denote that ai highly appreciates bk , while
negative values express dislike, respectively.
– Taxonomy C over set D = {d1 , d2 , . . . , dl }. Set D contains categories for product
classification. Each category de ∈ D represents one specific topic that products bk ∈
B may fall into. Topics express broad or narrow categories. The partial taxonomic
order C : D → 2D retrieves all immediate sub-categories C(de ) ⊆ D for topics
de ∈ D. Hereby, we require that C(de )∩C(dh ) = ∅ holds for all de , dh ∈ D, e = h,
hence imposing tree-like structuring, similar to single-inheritance class hierarchies
known from object-oriented languages. Leaf topics de are topics with zero outdegree,
formally C(de ) = ⊥, i.e., most specific categories. Furthermore, taxonomy C has
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exactly one top element , which represents the most general topic and has zero
indegree.
– Descriptor assignment function f : B → 2D . Function f assigns a set Dk ⊆
D of product topics to every product bk ∈ B. Note that products may possess
several descriptors, for classification into one single category generally entails loss
of precision.
We suppose all information about agents ai , their trust relationships ti and ratings
ri stored in machine-readable homepages distributed throughout the Web. Contrarily,
taxonomy C, set B of products and descriptor assignment function f must hold globally
and therefore offer public accessibility. Central maintenance of this information hence
becomes inevitable. Later on, we will demonstrate that such sources of information for
product categorization already exist for certain application domains.
3.2
Trust-Based Neighborhood Formation
The computation of trust-based neighborhoods constitutes one pivotal pillar of our approach. Clearly, neighborhoods are subjective, reflecting every agent ai s very beliefs
about the accorded trustworthiness of immediate peers. The incorporation of trust-based
social networks basically addresses two of the above-stated issues, namely security and
credibility of information provided, and computational complexity:
Maintaining Security and Credibility. Trust renders automatic recommendation generation for ai secure, making ai rely upon opinions from peers deemed trustworthy only.
Note that in general, collaborative filtering tends to be highly susceptive to manipulation. For instance, malicious agents aj can accomplish high similarity with ai simply
by copying its rating profile [16].
Dealing with application scenarios subject to central control and monitoring, attacks
on recommender systems only pose minor concerns. Contrarily, on the Semantic Web,
where, according to Tim Berners-Lee, “anyone can say anything about anything”, attackresistance and robustness may become an important criterion for recommender systems.
Hence, as Marsh [11] already indicated, trust makes agents “less vulnerable to others”.
Computational Complexity Reduction. However, for our scenario, trust also serves
another purpose, namely that of detecting similar peers. Neighborhood formation based
upon common collaborative filtering techniques requires O(|A|2 ) time complexity and
thus lacks scalability for decentralized scenarios. Contrarily, trust-driven neighborhood
formation schemes actually do scale well. However, substituting common neighborhood
formation approaches only makes sense when ensuring that trust also reflects similarity
to a certain extent:
Recent studies by Swearingen and Sinha [21] provided empirical evidence that people
tend to rely upon recommendations received from trusted fellows, i.e., friends, family
members etc., more than upon online recommender systems. Moreover, own research
endeavors [22] revealed that trust and interest similarity tend to positively correlate,
justifying trust as an appropriate supplement or surrogate for collaborative filtering.
Results from social psychology back our findings, since positive association between
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http://www.atu.edu/
http://www.electricminds.org/
http://homepage.mac.com/dave_rogers/
http://jacksseacabin.editthispage.com/
http://www.vfth.com/
http://hal.editthispage.com/
http://www.dangerousmeta.com/
http://instructionaltechnology.editthispage.com/
http://lawlady.editthispage.com/
http://www.metrocake.com/
http://werehosed.editthispage.com/
http://limit.elusive-heaven.net/
http://www.dantewoo.com/
http://www.unpolished.org/
http://www.sturtle.com/index2.html
http://www.tommyslim.com/
http://www.patricking.com/lost/
http://www.jonno.com/now/
http://onnatachi.pitas.com/
http://www.dantewoo.com/blog/
http://farseer.elusive-heaven.net/
http://spinel.pitas.com/
http://www.numberporn.com/
http://www.lunesse.com/journal.htm
http://www.cygnoir.net/quill/
http://www.cygnoir.net/
http://www.parkly.com/
http://home.iprimus.com.au/michellenet/
http://michellenet.blogspot.com/
F g 1 A Consum ng s a ges
us ne wo k c us e
a ud na s m ar y and n erpersona a rac on has effec ve y become one of h s
d sc p ne’s mos re ab e find ngs [3]
Massa [12] conduc ed exper men s on op of he we -known Ep n ons ra ng commun y (h p: www ep n ons com) revea ng ha s m ar y compu a on based upon
common co abora ve fi er ng approaches fa s when suppos ng very arge produc se s
hence mak ng ne ghborhood forma on mposs b e Opera ng on he same da a Massa
found ha “ rus -aware echn ques can produce rus scores for very h gh numbers of
peers” Ne ghborhood forma on hus becomes more fac e o ach eve
F gure 1 shows he arges c us er of he A Consum ng commun y rus ne work v sua zed w h ayou ng oo s we mp emen ed for ha purpose No e ha approx ma e y
95% of a members are par of he a er c us er so a ng compara ve y few peers and
eve ng he ground for effec ve ne ghborhood forma on
Trust Propagat on Mode s Trus -based ne ghborhood de ec on for a us ng hose
“ rus -aware echn ques” men oned by Massa mp es der v ng rus va ues for peers
aj no d rec y rus ed by a bu one of he persons he a er agen rus s d rec y or
nd rec y No e ha func ons t (aj ) are common y sparse prov d ng va ues for on y few
aj compared o A’s overa commun y s ze Hence rus me r cs exp o he “cond ona
rans v y” proper y of rus [1] and a ow for render ng rus func ons dense
Numerous sca ar me r cs [4 10] have been proposed for compu ng rus be ween
wo g ven nd v dua s a and aj We hereby deno e compu ed rus we gh s by tc (aj ) as
opposed o exp c rus t (aj ) However our approach requ res me r cs ha compu e
neares rus ne ghbors and no eva ua e rus va ues for any wo g ven agen s We hence
op for oca group rus me r cs [23] wh ch have on y been a rac ng marg na n eres
un now The mos mpor an and mos we -known oca group rus me r c s Lev en’s
83
Advogato metric [10]. However, the metric can only make boolean decisions with respect
to trustworthiness, classifying agents into trusted and untrusted ones.
Appleseed [23], our own novel proposal for local group trust computation, allows
more fine-grained analysis, assigning continuous trust weights for peers within trust
computation range. Rankings thus become feasible. Appleseed’s principal concepts derive from spreading activation models [18], which have been conceived for modelling
human semantic memory. Appleseed operates on partial trust graph information, exploring the social network within predefined ranges only and allowing the neighborhood
detection process ro retain scalability. Hereby, high ranks are accorded to trustworthy
peers, i.e., those agents which are largely trusted by others with high trustworthiness,
similar to PageRank [17]. These ranks are used later on for selecting agents deemed
suitable for making recommendations.
3.3
Taxonomy-Driven Similarity Metrics
Trust allows selecting peers with overall above-average interest similarity. However, for
each active user, some peers having completely opposed interests generally exist. The
proposition that interpersonal attraction, and hence trust, implies attitudinal similarity
does not always hold true. Supplementary filtering thus becomes indispensable. Two
approaches are conceivable:
– User-User Closeness. Additional filtering based upon profile similarity c(ai , aj )
between agents ai , aj is applied to the neighborhood of ai ’s trustworthy peers. The
resulting set of peers only contains trusted peers which are guaranteed to resemble
the active user ai .
– Product-User Relevance. Instead of pruning the very neighborhood, one could also
consider the set of all products bk appreciated by at least one of ai ’s neighbors and
dispose of those products not fitting the interest profile of ai . We hereby denote the
relevance of bk for ai by cb (ai , bk ).
For both cases, the filtering task faces the problem of low profile overlap by virtue
of information sparseness and potentially large product sets [12]. In order to alleviate
the prevailing issue, we propose taxonomy-driven profile computation [22, 24], which
allows to derive similarity between users ai and aj even though these peers have not rated
one product in common. Moreover, our novel filtering method also permits to compute
similarity between two products bk , bh .
Profile Generation. Our approach to taxonomy-driven generation of interest profiles
[22, 24] extends basic ideas derived from Middleton’s ontology-enhanced content-based
filtering method [13]. In contrast to generic feature-based filtering, product categories
still play an important role, but we have them arranged in a taxonomy and not separate
from each other. Products bk bear topic descriptors dke ∈ f (bk ) that relate these bk to
taxonomic nodes. Several classifications per product are possible, hence |f (bk )| ≥ 1.
Each product the user likes infers some interest score for those dke ∈ f (bk ). Since these
categories dke are arranged in taxonomy C, we can also infer a fractional interest for all
super-topics of dke . Hereby, remote super-topics are accorded less interest score than
super-topics close to dke . Assume that (p0 , p1 , . . . , pq ) gives the path from top element
84
C.-N. Ziegler
Books
Science
Archaelogy
Astronomy
Nonfiction
Reference
Medicine
Mathematics
Applied
Pure
Discrete
Sports
History
Algebra
Fig. 2. Small fragment from the Amazon.com book taxonomy
p0 = to node pq = dke . Function sib(p) returns the number of p’s siblings, while
sco(p) returns p’s score:
sco(pm+1 )
(3)
∀m ∈ {0, 1, . . . , q − 1} : sco(pm ) = κ ·
sib(pm+1 ) + 1
Scores are normalized, i.e., all topic score that ai ’s profile assigns to nodes from
taxonomy C amounts to some fixed value s. Hence, high product ratings from agents
with short product rating histories have higher impact on profile generation than product
ratings from persons issuing rife ratings. Score s is divided evenly among all products that
contribute to ai ’s profile makeup. Factor κ permits fine-tuning the extent of super-topic
score inference, depending on the underlying taxonomy’s depth and granularity.
Example 1 (Topic Score Assignment). Suppose the taxonomy given in Figure 2 which
represents a tiny fragment from the Amazon.com book taxonomy, and propagation factor
κ = 1. Let user ai have mentioned 4 books, namely Matrix Analysis, Fermat’s Enigma,
Snow Crash, and Neuromancer. For Matrix Analysis, 5 topic descriptors are given, one
of them pointing to leaf topic Algebra within our small taxonomy.
Suppose that s = 1000 defines the overall accorded profile score. Then the score
assigned to descriptor Algebra amounts to s / (4 · 5) = 50. Ancestors of leaf Algebra
are Pure, Mathematics, Science, and top element Books. Score 50 hence must be
divided among these topics according to Equation 3. Score 29.087 becomes accorded to
topic Algebra. Likewise, we get 14.543 for topic Pure, 4.848 for Mathematics, 1.212
for Science, and 0.303 for top element Books. These values are then used to build the
profile vector of user ai .
Success or failure of our approach largely depends upon taxonomy C used for classification. The more thoroughly crafted and fine-grained the latter taxonomy, the more
meaningful our profile information becomes. Clearly, topic descriptors f (bk ) for products bk must be chosen skillfully, too. By virtue of inference of fractional interest for
super-topics, one may establish high user similarity for users which have not even rated
one single product in common, as has been indicated before. According to our scheme,
the more score two profiles have accumulated in same branches, the higher their computed similarity.
85
Similarity Computation. Taxonomy-driven interest profiles form the grounding for
our novel filtering paradigm. Similarity computation between agents ai , aj , and between
agents ai and products bk 1 , respectively, requires some distance metric.
For our approach, we apply common nearest-neighbor techniques, namely Pearson
correlation [6, 20] and cosine distance known from information retrieval. Hereby, profile
vectors map category score vectors from C instead of plain product-rating vectors. High
similarity evolves from interest in many identical or related branches, whereas negative
correlation indicates diverging interests.
For instance, suppose ai reads literature about Applied Mathematics only, and
aj about Algebra, then their computed similarity will be high, considering significant
branch overlap from node Mathematics onward.
3.4
Recommendation Generation
We already indicated that two alternative designs are viable for post-filtering trust
neighborhoods. We opt for product-user relevance, i.e., deferring supplementary
neighborhood filtering into the recommendation process. We hence consider all peers
part of the trust neighborhood, but weed out products not matching the active user
ai ’s profile.
The relevance of some product bk one of ai ’s trusted peers recommends then depends
on various factors, the two most important aspects being the following ones:
– Accorded trust tci (aj ) of peers aj mentioning bk . Trust-based neighborhood formation substitutes finding nearest neighbors based upon interest similarity. Likewise,
similarity ranks c(ai , aj ) become replaced by trust weights tci (aj ) for computing
the predicted relevance of bk for ai .
– Content-based relevance cb (ai , bk ) of product bk for user ai . Besides mere trustworthiness of peers aj rating product bk , the content-based relevance of bk for the
active user ai is likewise important, e.g., one may consider the situation where even
close friends recommend products not fitting our interest profile at all.
Since both functions, i.e., tci (aj ) and cb (ai , bk ), operate on completely different
scales, the conversion of these absolute weights into ranks seems appropriate. For instance, the trusted agent aj with highest trust weight tci (aj ) obtains rank 1, and so forth.
Likewise, the product bk with highest similarity cb (ai , bk ) to the active user ai ’s interest
profile becomes top-ranked.
Besides merging trustworthiness and content-based relevance ranks, other factors
involved comprise product rating frequency and product description richness. Product
rating frequency intends to reward products bk recommended by numerous trusted peers,
while description richness penalizes products bearing overly general taxonomic descriptions. The final ranks of products bk eventually constitute the foundation for assembling
top-N recommendation lists for the active user ai .
1
Supposing implicit product ratings only, the generation of taxonomy-driven profiles
for products bk equates profile generation for pseudo-user aθ having implicitly rated bk
only.
86
3.5
C.-N. Ziegler
Supplementary Fine-Tuning
Taxonomy-driven profile generation renders another mechanism, dubbed “topic diversification” [24], feasible. Hereby, our novel approach, optionally applicable on top of
recommendation generation, allows rearrangement of the active user ai ’s recommendation list in order to better reflect ai ’s full range of interests, considering the impact
of specific topics ai implicitly declares interest in. For instance, suppose that novels
classifying under Modern German Poetry have twice the share of Social Psychology in
a i’s reading list. Then post-processing of ai ’s recommendation list by means of topic
diversification procedures allows to fully account for that fact.
To our best knowledge, no similar approaches exist or have been documented in
literature affiliated with recommender systems. Moreover, topic diversification becomes
even more valuable when making recommendations across diverse domains of interest,
e.g., books, DVDs, apparel, etc.
Other enhancements include considering explicit product ratings for recommendation generation whenever available. However, note that most scenarios only allow for
collecting implicit ratings, e.g., purchase data, product mentions, etc., rather than explicit
ones.
4 Real-World Deployment
Section 3.1 exposed our envisioned information infrastructure. We will show that such
an architecture may actually come into life and become an integral part of the Semantic
Web:
– Social Networks. FOAF defines machine-readable homepages based upon RDF
and allows weaving acquaintance networks. Golbeck [5] proposed some modifications making FOAF support “real” trust relationships instead of mere acquaintanceship.
– Product Rating Information. Moreover, FOAF networks seamlessly integrate with
so-called “weblogs”, which are steadily gaining momentum. These personalized
“online diaries” are especially valuable with respect to product rating information.
For instance, some crawlers extract certain hyperlinks from weblogs and analyze
their makeup and content. Hereby, those referring to product pages from large catalogs like Amazon.com (http://www.amazon.com) count as implicit votes for these
goods. Mappings between hyperlinks and some sort of unique identifier are required
for diverse catalogs, though. Unique identifiers exist for some product groups like
books, which are given “International Standard Book Numbers”, i.e., ISBNs. Efforts to enhance weblogs with explicit, machine-readable rating information have
also been proposed and are becoming increasingly popular. For instance, BLAM!
(http://www.pmbrowser.info/hublog/ ) allows creating book ratings and helps embedding these into machine-readable weblogs.
– Product Classification Taxonomies. Besides user-centric information, i.e., agent
ai ’s trust relationships ti and product ratings ri , taxonomies for product classification play an important role within our approach. Luckily, these taxonomies exist for
87
certain domains. Amazon.com defines an extensive, fine-grained and deeply-nested
taxonomy for books, containing thousands of topics. More important, Amazon.com
provides books with subject descriptors referring to the latter taxonomy. Similar
taxonomies exist for DVDs, CDs, and videos. Standardization efforts for product
classification are channelled through the “United Nations Standard Products and
Services Code” project (http://www.unspsc.org/ ). However, the UNSPSC’s taxonomy provides much less information and nesting than, for instance, Amazon.com’s
taxonomy for books.
5 Experimental Setting
We created an experimental environment simulating the infrastructure proposed above.
Hereby, by means of crawlers and screen scrapers, we gathered information from various trust-aware online communities like All Consuming (http://www.allconsuming.net),
and Advogato (http://www.advogato.org), extracting information about approximately
10, 000 users ai , their trust relationships ti and implicit product ratings ri . Ratings were
obtained from All Consuming only.
Moreover, we captured Amazon.com’s huge book taxonomy, made up of 13, 525
hierarchically arranged topics de , and categorization data about 11, 031 books bk that
All Consuming community members mentioned. Tailored crawlers search the Web for
weblogs and ensure data freshness. All our experiments and empirical evaluations were
based upon this “real-world” data.
6 Related Work
Recommender systems have begun attracting major research interest during the early
nineties [6]. Nowadays, commercial and industrial systems are rife and wide-spread,
detailed comparisons concerning features and approaches are given in [19]. Recommender systems differ from each other mainly through their filtering method. Hereby,
distinctions between three types of filtering systems are made [6], namely collaborative,
content-based and economic. Collaborative filtering systems [20] generate recommendations obtained from persons having similar interests. Content-based filtering only takes
into account descriptions of products, based upon metadata and extracted features. Economic filtering has seen little practical application until now and exerts marginal impact
only.
Modern recommender systems are hybrid, combining both content-based and collaborative filtering facilities in one single framework. Fab [2] counts among the first
popular hybrid systems, more recent approaches have been depicted in [7] and [13].
Our filtering approach, comprising taxonomy-based profile generation and similarity
computation, also exploits both content-based and collaborative filtering facilities. Trust
networks add another supplementary level of filtering.
Initial attempts have been taken towards transplanting recommender systems into
decentralized scenarios. Olsson [15] gives an extensive overview of existing approaches.
Kautz et al. [9] extract implicit social network structures from the Web, using them
88
C.-N. Ziegler
as foundations for recommender system services operating on top of these networks.
Jensen et al. [8] propose an approach called “explicit peer-based systems”, which makes
recommendations based upon friends’ opinions.
7 Future Directions
Our past efforts have mainly focused on designing suitable trust metrics for computing
trust neighborhoods [23], and conceiving metrics for making collaborative filtering applicable to decentralized architectures [22]. Moreover, we have shaped and synthesized
an extensive infrastructure based upon “real-world” data from various communities and
online stores.
Until now, our analysis has been largely confined to the book domain only. Future
research will also include movies and other specific product groups and investigate the
intrinsic differences between these groups. For instance, Amazon.com’s taxonomy for
DVD classification contains more topics than its book counterpart, though being less
deep. We would like to better understand the impact that taxonomy structure may have
upon profile generation and similarity computation.
Moreover, owing to the fact that our novel taxonomy-driven filtering approach yields
excellent results compared to generic benchmark systems when dealing with information
sparseness [24], we are planning to investigate our filtering paradigm’s performance
when applied to dense product rating datasets, likewise.
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Semantic Web Recommender Systems

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