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  • Bibliography

    [1] Abdul-Rahman, A. and Hailes, S. (2000). Supporting trust in virtual communities, in Proceed-ings of the 33rd Hawaii International Conference on System Sciences, pp. 1769–1777.

    [2] Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender sys-tems: a survey of the state-of-the-art and possible extensions, IEEE Transactions onKnowledge and Data Engineering , pp. 734–749.

    [3] Ahn, H. (2008). A new similarity measure for collaborative filtering to alleviate the new usercold-starting problem, Information Sciences 178, pp. 37–51.

    [4] Almenárez, F., Marı́n, A., Campo, C. and Garcı́a, C. (2004). PTM: A pervasive trust manage-ment model for dynamic open environments, in Proceedings of the First Workshop onPervasive Security, Privacy and Trust, in conjuntion with Mobiquitous.

    [5] Arazy, O., Elsane, I., Shapira, B. and Kumar, N. (2007). Social relationships in recommendersystems, in Proceedings of the 17th Workshop on Information Technologies & Systems,pp. 146–151.

    [6] Arazy, O., Kumar, N. and Shapira, B. (2009). Improving social recommender systems, ITProfessional 11, pp. 38–44.

    [7] Artz, D. and Gil, Y. (2007). A survey of trust in computer science and the semantic web,Journal of Web Semantics 5, pp. 58–71.

    [8] Arulselvan, A., Commander, C., Elefteriadou, L. and Pardalos, P. (2009). Detecting criticalnodes in sparse graphs, Computers and Operations Research 36, pp. 2193–2200.

    [9] Atanassov, K. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20, pp. 87–96.[10] Belnap, N. (1977a). How a computer should think, Ryle, G. (ed.) Contemporary Aspects of

    Philosophy, pp. 30–56.[11] Belnap, N. (1977b). A useful four-valued logic, Epstein, G., and Dunn, J. (eds.) Modern Uses

    of Multiple-Valued Logics, pp. 7–37.[12] Berners-Lee, T., Hendler, J. and Lassila, O. (2001). The semantic web, Scientific American

    284, pp. 35–43.[13] Blabanović, M. and Shoham, Y. (1997). Fab: content-based, collaborative recommendation,

    Communications of the ACM 40, pp. 66–72.[14] Borgatti, S. (2006). Identifying sets of key players in social networks, Computational and

    Mathematical Organizational Theory 12, pp. 21–34.[15] Burke, R. (2002). Hybrid recommender systems: survey and experiments, User Modeling and

    User-Adapted Interaction 12, pp. 331–370.[16] Cacioppo, J. and Berntson, G. (1994). Relationship between attitudes and evaluative space:

    a critical review, with emphasis on the separability of positive and negative substrates,Psychological Bulletin 115, pp. 401–423.

    [17] Castelfranchi, C. and Falcone, R. (2001). Social trust: a cognitive approach, Castelfranchi, C

    193

  • 194 Trust Networks for Recommender Systems

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  • Subject Index

    accessible item, 162accuracy change, 162

    betweenness, 164betweenness utility, 165bilattice, 16bipolarity, 13

    cold start problem, 96, 154collaborative filtering, 5, 94, 110connector, 158content-based filtering, 5, 93controversial item, 105controversiality, 102copy-profile attack, 97CouchSurfing, 2, 43, 75, 99coverage, 101

    debugged trust-based collaborative filtering,139

    debugged trust-based weighted mean, 139distrust, 12distrust boundary preservation, 56distrust monotonicity, 58distrust ordering, 19distrust-based collaborative filtering, 142distrust-based filtering, 138

    Eigentrust, 11EnsembleTrustCF, 131entropy, 104Epinions, 2, 10, 25, 43, 75, 99, 126, 143, 157

    FATP, 54FilmTrust, 99, 114, 184FPTA, 54fragmentation, 166

    frequent rater, 158fuzzy logic, 11fuzzy relation, 21fuzzy set, 21

    Grouplens, 93

    idempotency, 56ignorance, 14, 34, 59inconsistency, 14, 60induced ordered weighted average, 53

    Kevin Bacon Oracle, 24knowledge absorption, 31knowledge boundary preservation, 56knowledge defect, 17knowledge monotonicity, 29, 57

    leave-one-out method, 46level of disagreement, 105

    maven, 158mean absolute error, 46, 100Moleskiing, 114MoleTrust, 120Movielens, 98multi-agent system, 9

    negator, 37Netflix, 5, 98

    opposite arguments condition, 60ordered weighted average, 52orness degree, 53

    Pearson’s correlation coefficient, 95

    201

  • 202 Trust Networks for Recommender Systems

    propagation horizon, 121

    recommender system, 4, 25, 33, 91room for originality, 168root mean square error, 75, 100

    Semantic Web, 9small world phenomenon, 24small-network theory, 24social network, 1standard deviation, 102subjective logic, 11, 16, 54

    TidalTrust, 115triangular conorm, 37triangular norm, 35trust aggregation, 3, 53

    associativity, 59K-IOWA, 74K-OWA, 65KAAV, 69KK-OWA, 71knowledge maximizing operator, 64knowledge preference averaging operator, 64operators, 61trust maximizing operator, 63

    trust boundary preservation, 56trust decay, 28

    trust metric, 3, 25trust model

    gradual, 11probabilistic, 11

    trust monotonicity, 30, 58trust network, 2, 20trust ordering, 19trust propagation, 3, 26

    associativity, 30atomic, 26cocitation, 26operators, 39transitivity, 26transpose trust, 26trust coupling, 26

    trust provenance, 13trust score, 16

    consistent, 31, 42, 57incomplete, 17inconsistent, 17

    trust score space, 17trust-based collaborative filtering, 119trust-based filtering, 124trust-based weighted mean, 115trust-distrust monotonicity, 57trusted third party, 26

    Web of Trust, 5, 10, 25

    BibliographySubject Index