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

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc, have attracted millions of users to share their social friendship and their locations via check-ins The available check-in information makes it possible to mine users' preference on locations and to provide favorite recommendations Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services To solve this task, matrix factorization is a promising tool due to its success in recommender systems However, previously proposed matrix factorization (MF) methods do not explore geographical influence, eg, multi-center check-in property, which yields suboptimal solutions for the recommendation In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs We first capture the geographical influence via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM) Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework Our solution to POI recommendation is efficient and scales linearly with the number of observations Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly

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Fused matrix factorization with geographical and social influence in location-based social networks

Personalized point-of-interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help provide better user experience as well as enable third-party services, e.g., launching advertisements. To provide a good recommendation, various research has been conducted in the literature. However, pervious efforts mainly consider the "check-ins" in a whole and omit their temporal relation. They can only recommend POI globally and cannot know where a user would like to go tomorrow or in the next few days. In this paper, we consider the task of successive personalized POI recommendation in LBSNs, which is a much harder task than standard personalized POI recommendation or prediction. To solve this task, we observe two prominent properties in the check-in sequence: personalized Markov chain and region localization. Hence, we propose a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions. Our proposed FPMC-LR not only exploits the personalized Markov chain in the check-in sequence, but also takes into account users' movement constraint, i.e., moving around a localized region. More importantly, utilizing the information of localized regions, we not only reduce the computation cost largely, but also discard the noisy information to boost recommendation. Results on two real-world LBSNs datasets demonstrate the merits of our proposed FPMC-LR.

Where you like to go next: successive point-of-interest recommendation

Recommendation techniques have been well developed in the past decades. Most of them build models only based on user item rating matrix. However, in real world, there is plenty of auxiliary information available in recommendation systems. We can utilize these information as additional features to improve recommendation performance. We refer to recommendation with auxiliary information as context-aware recommendation. Context-aware Factorization Machines (FM) is one of the most successful context-aware recommendation models. FM models pairwise interactions between all features, in such way, a certain feature latent vector is shared to compute the factorized parameters it involved. In practice, there are tens of context features and not all the pairwise feature interactions are useful. Thus, one important challenge for context-aware recommendation is how to effectively select "good" interaction features. In this paper, we focus on solving this problem and propose a greedy interaction feature selection algorithm based on gradient boosting. Then we propose a novel Gradient Boosting Factorization Machine (GBFM) model to incorporate feature selection algorithm with Factorization Machines into a unified framework. The experimental results on both synthetic and real datasets demonstrate the efficiency and effectiveness of our algorithm compared to other state-of-the-art methods.

Gradient boosting factorization machines

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users’ moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users’ preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users’ moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user’s check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top-k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users’ preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

The invention discloses a characteristic recommendation method and a characteristic recommendation device. The characteristic recommendation method comprises the following steps: according to an output textual characteristic estimation model, determining target values of textual characteristics in sample data, wherein the output textual characteristic estimation model is obtained according to the optimal combined characteristics selected from training data; according to the target values, sorting the textual characteristics in the sample data and according to a sequence of the target value from high to low, carrying out recommendation on the textual characteristics in the sample data. The characteristic recommendation method and the characteristic recommendation device can realize automatic selection of the effective combined characteristics, are time-saving and labor-saving, effectively solve the difficult problems of time waste and labor waste in the existing manual characteristic selecting process and can improve effectiveness of a recommendation system.

Chen Cheng

Papers

Fused matrix factorization with geographical and social influence in location-based social networks

Where you like to go next: successive point-of-interest recommendation

Gradient boosting factorization machines

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Characteristic recommendation method and device