Enhancing Accuracy of Recommender Systems through various approaches to Local and Global Similarity Measures

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{Anand:thesis,
  author =       "Deepa Anand",
  title =        "Enhancing Accuracy of Recommender Systems through
                 various approaches to Local and Global Similarity
                 Measures",
  year =         "2011",
  school =       "Computer and System Sciences, Jawaharlal Nehru
                 University",
  address =      "New Delhi, India",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, recommender
                 systems",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Anand_thesis.pdf",
  size =         "172 pages",
  abstract =     "Web 2.0 represents a paradigm shift in the way that
                 internet is consumed. Users' role has evolved from that
                 of passive consumers of content to active prosumers,
                 implying a plethora of information sources and an ever
                 increasing ocean of content. Collaborative Recommender
                 systems have thus emerged as Web 2.0 personalisation
                 tools which aid users in grappling with the overload of
                 information by allowing the discovery of content in
                 contrast to plain search popularised by prior web
                 technologies. To this end Collaborative filtering (CF)
                 exploit the preferences of users who have liked similar
                 items in the past to help a user to identify
                 interesting products and services. The success of CF
                 algorithms, however, is hugely dependent on the
                 technique designed to determine the set of users whose
                 opinion is sought. Traditionally user closeness is
                 assessed by matching their preferences on a set of
                 common experiences that both share. The challenge with
                 this kind of computation is the overabundance of
                 available content to be experienced, at the user's
                 disposal, thus rendering the user-preference space very
                 sparse. The similarity so computed is thus unstable for
                 user pairs sharing a small set of experiences and is in
                 fact incomputable for most user pairs due to a lack of
                 expressed common preferences.

                 To remedy the sparsity problems we propose methods to
                 enrich the set of user connections obtained using
                 measures such as Pearson Correlation Coefficient (PCC)
                 and Cosine Similarity (COS). We achieve this by
                 leveraging on explicit trust elicitation and trust
                 transitivity. When interacting with anonymous users
                 online, in the absence of physical cues apparent in our
                 daily life, trust provides a reliable measure of
                 quality and guides the user decision process on whether
                 or not to interact with an entity. These trust
                 statements in addition to identifying malicious users
                 also enhance user connectivity by establishing links
                 between pairs of users whose closeness cannot be
                 determined through preference data. In addition
                 transitivity of trust can also be leveraged to further
                 expand the set of neighbours to collaborate with. We
                 first explore a bifurcated view of trust: functional
                 and referral trust i.e. trust in an entity to recommend
                 items and the trust in an entity to recommend
                 recommenders and propose models to quantify referral
                 trust. Such a referral-functional trust framework leads
                 to more meaningful derivation of trust through
                 transitivity resulting in better quality
                 recommendations.

                 Though trust has been extensively used in literature to
                 support the CF process, distrust information has been
                 explored very little in this context. We thus propose a
                 tri-component computation of trust and distrust using
                 preference, functional trust and referral trust in
                 order to densify the network of user interconnections.
                 To maintain a balance between increased coverage and
                 the quality of recommendations, however, we quantify
                 risk measures for each trust and distrust relationship
                 so derived and prune the network to retain high quality
                 relationships thus ensuring good connections formed
                 between users through transitivity of trust and
                 distrust.

                 In the absence of supplemental information such as
                 trust/distrust to provide extra knowledge about user
                 links the local similarity connections can be harnessed
                 to deem a pair of users similar if they are share
                 preferences with the same set of users thus estimating
                 the global similarity between user pairs. We
                 investigate the effectiveness of various graph based
                 global or indirect similarity computation schemes in
                 enhancing the user or item neighborhood thus bettering
                 the quality and number of recommendations obtained.",
  abstract =     "In addition to the inadequacy of similarity measures
                 such as PCC and VS in forming a rich user neighbourhood
                 they are static and may not capture user matching
                 satisfactorily and guarantee optimal performance under
                 diverse data situations. We propose to learn similarity
                 measures which not only adjust to the type of data at
                 hand but also ensure optimal performance over time.
                 Evolutionary techniques are employed to learn such
                 adaptive similarity measures.

                 Finally sparsity variant fusion of predictions from
                 local and global similarity measures have been shown to
                 offer quality recommendations. In particular the fact
                 that local similarity measures suffice when the
                 preference data is dense but overtaken in performance
                 by global similarity links when preference data is
                 scarce can be leveraged to fuse the recommendations
                 from the two systems. We define sparsity not only for
                 the overall system but also at the user and user-item
                 level. We use these measures to suggest a fusion scheme
                 tailored for each user and/or for each item to be
                 predicted by estimating the apportionment of influence
                 local and global similarity measures have on each
                 prediction.

                 We demonstrate the effectiveness of the proposed
                 techniques through experiments performed on real world
                 datasets.",
  notes =        "Supvervisor: K. K. Bharadwaj. JNU",
}

Genetic Programming entries for Deepa Anand

Citations