Paper Session 10: Social Perspective

Date: Sunday, Sept 18, 2016, 16:20-18:00
Location: Kresge Auditorium
Chair: Elizabeth Daly

• LPRecommending New Items to Ephemeral Groups Using Contextual User Influence
  by Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca

  Group recommender systems help groups of users in finding appropriate items to be enjoyed together. Lots of activities, like watching TV or going to the restaurant, are intrinsically group-based, thus making the group recommendation problem very relevant. In this paper we study ephemeral groups, i.e., groups where the members might be together for the first time. Recent approaches have tackled this issue introducing complex models to be learned offline, making them unable to deal with new items; on the contrary, we propose a group recommender able to manage new items too. In more detail, our technique determines the preference of a group for an item by combining the individual preferences of the group members on the basis of their contextual influence, where the contextual influence represents the ability of an individual, in a given situation, to direct the group’s decision. We conducted an extensive experimental evaluation on a TV dataset containing a log of viewings performed by real groups, showing how our approach outperforms the comparable techniques from the literature.

  Full text in ACM Digital Library

• LPGuided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks
  by Roy Levin, Hassan Abassi, Uzi Cohen

  Online social networks have become predominant in recent years and have grown to encompass massive scales of data. In addition to data scale, these networks can be heterogeneous and contain complex structures between different users, between social entities and various interactions between users and social entities. This is especially true in enterprise social networks where hierarchies explicitly exist between employees as well. In such networks, producing the best recommendations for each user is a very challenging problem for two main reasons. First, the complex structures in the social network need to be properly mined and exploited by the algorithm. Second, these networks contain millions or even billions of edges making the problem very difficult computationally. In this paper we present Guided Walk, a supervised graph based algorithm that learns the significance of different network links for each user and then produces entity recommendations based on this learning phase. We compare the algorithm with a set of baseline algorithms using offline evaluation techniques as well as a user survey. The offline results show that the algorithm outperforms the next best algorithm by a factor of 3.6. The user survey further confirms that the recommendation are not only relevant but also rank high in terms of personal relevance for each user. To deal with large scale social networks, the Guided Walk algorithm is formulated as a Pregel program which allows us to utilize the power of distributed parallel computing. This would allow horizontally scaling the algorithm for larger social networks by simply adding more compute nodes to the cluster.

  Full text in ACM Digital Library

• LPSTAR: Semiring Trust Inference for Trust-Aware Social Recommenders
  by Peixin Gao, Hui Miao, John S Baras, Jennifer Golbeck

  Social recommendation takes advantage of the influence of social relationships in decision making and the ready availability of social data through social networking systems. Trust relationships in particular can be exploited in such systems for rating prediction and recommendation, which has been shown to have the potential for improving the quality of the recommender and alleviating the issue of data sparsity, cold start, and adversarial attacks. An appropriate trust inference mechanism is necessary in extending the knowledge base of trust opinions and tackling the issue of limited trust information due to connection sparsity of social networks. In this work, we offer a new solution to trust inference in social networks to provide a better knowledge base for trust-aware recommender systems. We propose using a semiring framework as a nonlinear way to combine trust evidences for inferring trust, where trust relationship is model as 2-D vector containing both trust and certainty information. The trust propagation and aggregation rules, as the building blocks of our trust inference scheme, are based upon the properties of trust relationships. In our approach, both trust and distrust (i.e., positive and negative trust) are considered, and opinion conflict resolution is supported. We evaluate the proposed approach on real-world datasets, and show that our trust inference framework has high accuracy, and is capable of handling trust relationship in large networks. The inferred trust relationships can enlarge the knowledge base for trust information and improve the quality of trust-aware recommendation.

  Full text in ACM Digital Library

• LPVista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
  by Ruining He, Chen Fang, Zhaowen Wang, Julian McAuley

  Understanding users’ interactions with highly subjective content—like artistic images—is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard’ recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create. In this paper we build large-scale recommender systems to model the dynamics of a vibrant digital art community, Behance, consisting of tens of millions of interactions (clicks and `appreciates’) of users toward digital art. Methodologically, our main contributions are to model (a) rich content, especially in terms of its visual appearance; (b) temporal dynamics, in terms of how users prefer `visually consistent’ content within and across sessions; and (c) social dynamics, in terms of how users exhibit preferences both towards certain art styles, as well as the artists themselves.

  Full text in ACM Digital Library

• LPRepresentation Learning for Homophilic Preferences
  by Trong T. Nguyen, Hady W. Lauw

  Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek to learn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be effective for large preference datasets is Restricted Boltzmann Machine (RBM). While homophily, or the tendency of friends to share their preferences at some level, is an established notion in sociology, thus far it has not yet been clearly demonstrated on RBM-based preference models. The question lies in how to appropriately incorporate social network into the architecture of RBM-based models for learning representations of preferences. In this paper, we propose two potential architectures: one that models social network among users as additional observations, and another that incorporates social network into the sharing of hidden units among related users. We study the efficacies of these proposed architectures on publicly available, real-life preference datasets with social networks, yielding useful insights.

  Full text in ACM Digital Library

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