A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field

Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)

What if Information Retrieval (IR) systems did not just retrieve relevant information that is stored in their indices, but could also "understand" it and synthesise it into a single document? We present a preliminary study that makes a first step towards answering this question. Given a query, we train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21, 2016, Pisa, Ital

PACRR: A Position-Aware Neural IR Model for Relevance Matching

In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks.Comment: To appear in EMNLP201

HHMF: hidden hierarchical matrix factorization for recommender systems

Abstract(#br)Matrix factorization (MF) is one of the most powerful techniques used in recommender systems. MF models the (user, item) interactions behind historical explicit or implicit ratings. Standard MF does not capture the hierarchical structural correlations, such as publisher and advertiser in advertisement recommender systems, or the taxonomy (e.g., tracks, albums, artists, genres) in music recommender systems. There are a few hierarchical MF approaches, but they require the hierarchical structures to be known beforehand. In this paper, we propose a Hidden Hierarchical Matrix Factorization (HHMF) technique, which learns the hidden hierarchical structure from the user-item rating records. HHMF does not require the prior knowledge of hierarchical structure; hence, as opposed to..