Session 6: Recommendation Methods and Theory

Date:Wednesday, Oct 8, 16:30-18:00
Moderator: Alexander Felfernig

• Unifying Nearest Neighbors Collaborative Filtering

  by Koen Verstrepen and Bart Goethals

  We study collaborative filtering for applications in which there exists for every user a set of items about which the user has given binary, positive-only feedback (one-class collaborative filtering). Take for example an on-line store that knows all past purchases of every customer. An important class of algorithms for one-class collaborative filtering are the nearest neighbors algorithms, typically divided into user-based and item-based algorithms. We introduce a reformulation that unifies user- and item-based nearest neighbors algorithms and use this reformulation to propose a novel algorithm that incorporates the best of both worlds and outperforms state-of-the-art algorithms. Additionally, we propose a method for naturally explaining the recommendations made by our algorithm and show that this method is also applicable to existing user-based nearest neighbors methods.

• Recommending User Generated Item Lists

  by Yidan Liu, Min Xie and Laks V.S. Lakshmanan

  Existing recommender systems mostly focus on recommending individual items which users may be interested in. User-generated item lists on the other hand have become a popular feature in many applications. E.g., Goodreads provides users with an interface for creating and sharing interesting book lists. These user-generated item lists complement the main functionality of the corresponding application, and intuitively become an alternative way for users to browse and discover interesting items to be consumed. Unfortunately, existing recommender systems are not designed for recommending user-generated item lists. In this work, we study properties of these user-generated item lists and propose a Bayesian ranking model, called \LRM for recommending them. The proposed \LRM model takes into consideration users’ previous interactions with both item lists and with individual items. Furthermore, we propose in \LRM a novel way of weighting items within item lists based on both position of items, and personalized list consumption pattern. Through extensive experiments on real item list dataset from Goodreads, we demonstrate the effectiveness of our proposed \LRM model.

• Question Recommendation for Collaborative Question Answering Systems with RankSLDA

  by Jose San Pedro and Alexandros Karatzoglou

  Collaborative question answering (CQA) communities rely on user participation for their success. This paper presents a supervised Bayesian approach to model expertise in on-line CQA communities with application to question recommendation, aimed at reducing waiting times for responses and avoiding question starvation. We propose a novel algorithm called RankSLDA which extends the supervised Latent Dirichlet Allocation (sLDA) model by considering a learning-to-rank paradigm. This allows us to exploit the inherent collaborative effects that are present in CQA communities where users tend to answer questions in their topics of expertise. Users can thus be modeled on the basis of the topics in which they demonstrate expertise. In the supervised stage of the method we model the pairwise order of expertise of users on a given question. We compare RankSLDA against several alternative methods on data from the Cross Validate community, part of the Stack Exchange CQA network. RankSLDA outperforms all alternative methods by a significant margin.

• Bayesian Binomial Mixture Model for Collaborative Prediction with Non-Random Missing Data

  by Yong-Deok Kim and Seungjin Choi

  In real-world datasets, the presence of a certain amount of missing data is inevitable. If the data is not missing at random (MAR), the missing mechanism can not be ignored, and has to be modelled precisely as to obtain correct results. However, although there are strong possibilities of violation of MAR assumption on rating datasets collected from recommendation systems, most prior researches on collaborative prediction ignore the missing data mechanism. Exceptions are recent works on multinomial mixture model with CPT-v and Logit-vd, which employ conditional Bernoulli selection models for the response variables. In this paper, we present a Bayesian binomial mixture model for collaborative prediction with non-random missing data. We consider three factors for reason of observation: user, item, and rating value. Each factor is modelled by Bernoulli random variable, and the observation of rating is determined by the OR operation of three binary variables. Because of the property of OR operation, one of three factors can override others, hence it naturally models the 80-20 rule, which commonly arise in the recommendation systems. We develop efficient variational inference algorithms with closed-form update rules for all variational parameters, where the computational complexity depends on the number of observation, not on the size of the rating data matrix. Finally, we present experimental results showing that 1) binomial mixture model is more suitable than multinomial mixture model for modelling discrete, finite, and ordered rating values; 2) our model find meaningful solutions instead of boundary solutions, if hyper-parameters are estimated by empirical Bayes; 3) our model can capture different rating trend between domain (e.g. songs and movies).

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