Session 4a: Novel Setups
Date: Friday, Sept 18, 2015, 09:00-10:30
Location: HS 1
Chair: Dietmar Jannach
- It Takes Two to Tango: an Exploration of Domain Pairs for Cross-Domain Collaborative Filtering
by Shaghayegh Sahebi and Peter Brusilovsky
As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering aims to transfer the user rating pattern from source (auxiliary) domains to a target domain for the purpose of alleviating the sparsity problem and providing better target recommendations. However, the studies so far have either focused on a limited number of domains that are assumed to be related to each other (such as books and movies), or a division of the same dataset (such as movies) into different domains based on an item characteristic (such as genre). In this paper, we study a broad set of domains and their characteristics to understand the factors that affect the success or failure of cross-domain collaborative filtering, the amount of improvement in cross-domain approaches, and the selection of best source domains for a specific target domain. We propose to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggest a cross-domain collaborative filtering based on CCA (CD-CCA) that proves to be successful in using the shared information between domains in the target recommendations.
- Recommending Fair Payments for Large-Scale Social Ridesharing
by Filippo Bistaffa, Alessandro Farinelli, Georgios Chalkiadakis and Sarvapali D. Ramchurn
We perform recommendations for the Social Ridesharing scenario, in which a set of commuters, connected through a social network, arrange one-time rides at short notice. In particular, we focus on how much one should pay for taking a ride with friends. More formally, we propose the first approach that can compute fair coalitional payments that are also stable according to the game-theoretic concept of the kernel for systems with thousands of agents in real-world scenarios. Our tests, based on real datasets for both spatial (GeoLife) and social data (Twitter), show that our approach is significantly faster than the state-of-the-art (up to 84 times), allowing us to compute stable payments for 2000 agents in 50 minutes. We also develop a parallel version of our approach, which achieves a near-optimal speed-up in the number of processors used. Finally, our empirical analysis reveals new insights into the relationship between payments incurred by a user by virtue of its position in its social network and its role (rider or driver).
- Learning Distributed Representations from Reviews for Collaborative Filtering
by Amjad Almahairi, Kyle Kastner, Kyunghyun Cho and Aaron Courville
Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study their effect on collaborative filtering performance. While the previous state-of-the-art approach is based on a latent Dirichlet allocation (LDA) model of reviews, the models we explore are neural network based: a bag-of-words product-of-experts model and a recurrent neural network. We demonstrate that the increased flexibility offered by the product-of-experts model allowed it to achieve state-of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach. However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model’s ability to act as a regularizer of the product representations.