Matrix Factorization Techniques for Recommender Systems

Top-N sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-N ranked items that a user will likely interact in a »near future». The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model »Caser» as a solution to address this requirement. The idea is to embed a sequence of recent items into an »image» in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public data sets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.

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Time-aware point-of-interest recommendation

Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial contextual information is collected in systems, and the location prediction problem becomes crucial and feasible. Some works have been proposed to address this problem, but they all have their limitations. Factorizing Personalized Markov Chain (FPMC) is constructed based on a strong independence assumption among different factors, which limits its performance. Tensor Factorization (TF) faces the cold start problem in predicting future actions. Recurrent Neural Networks (RNN) model shows promising performance comparing with PFMC and TF, but all these methods have problem in modeling continuous time interval and geographical distance. In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). ST-RNN can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transition matrices for different geographical distances. Experimental results show that the proposed ST-RNN model yields significant improvements over the competitive compared methods on two typical datasets, i.e., Global Terrorism Database (GTD) and Gowalla dataset.

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Predicting the next location: a recurrent model with spatial and temporal contexts

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations However, extreme sparsity of user-POI matrices creates a severe challenge To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit weighted matrix factorization for this task since it usually serves collaborative filtering with implicit feedback better Besides, researchers have recently discovered a spatial clustering phenomenon in human mobility behavior on the LBSNs, ie, individual visiting locations tend to cluster together, and also demonstrated its effectiveness in POI recommendation, thus we incorporate it into the factorization model Particularly, we augment users' and POIs' latent factors in the factorization model with activity area vectors of users and influence area vectors of POIs, respectively Based on such an augmented model, we not only capture the spatial clustering phenomenon in terms of two-dimensional kernel density estimation, but we also explain why the introduction of such a phenomenon into matrix factorization helps to deal with the challenge from matrix sparsity We then evaluate the proposed algorithm on a large-scale LBSN dataset The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon into matrix factorization improves recommendation performance

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GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation

of thesis entitled: Learning to Recommend with Location and Context Submitted by CHENG, Chen for the degree of Doctor of Philosophy at The Chinese University of Hong Kong in September 2015 In the past decade, location-based social networks (LBSNs), such as Gowalla and Foursquare, have attracted millions of users to share their locations via check-in behaviors. Point-of-interest (POI) recommendation has been a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Different from traditional recommender systems in music and movie, POI recommendation needs to consider the geographical influence, which has a great impact on users’ choices. Apart from geo-information from locations, plenty of other auxiliary information is also available. All these auxiliary information are referred to as context. Recommendation with location and context brings new challenges for recommender systems and new methods need to be tailored for it. In this thesis, the challenges from three perspectives motivated by real life problems will be addressed. Firstly, we consider the point-of-interest recommendation task in LBSNs. In this task, we try to recommend the potentially attractive while unvisited POIs to users. By carefully studying the users’ moving patterns, we find that users tend to check in around several centers and different users have different number of centers. Based on this finding, we propose a novel Multi-center Gaussian Model to capture this pattern. Moreover, we consider the fact that users are usually more interested in the top 10 or 20 recommen-

Chen Cheng

Papers

Learning to Recommend with Location and Context