Weiqing Wang

Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important means to help people discover attractive and interesting venues and events, especially when users travel out of town. However, this recommendation is very challenging compared to the traditional recommender systems. A user can visit only a limited number of spatial items, leading to a very sparse user-item matrix. Most of the items visited by a user are located within a short distance from where he/she lives, which makes it hard to recommend items when the user travels to a far away place. Moreover, user interests and behavior patterns may vary dramatically across different geographical regions. In light of this, we propose Geo-SAGE, a geographical sparse additive generative model for spatial item recommendation in this paper. Geo-SAGE considers both user personal interests and the preference of the crowd in the target region, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items. To further alleviate the data sparsity issue, Geo-SAGE exploits the geographical correlation by smoothing the crowds preferences over a well-designed spatial index structure called spatial pyramid. We conduct extensive experiments and the experimental results clearly demonstrate our Geo-SAGE model outperforms the state-of-the-art.

Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, the Topic-Region Model (TRM), to simultaneously discover the semantic, temporal, and spatial patterns of users’ check-in activities, and to model their joint effect on users’ decision making for selection of POIs to visit. To demonstrate the applicability and flexibility of TRM, we investigate how it supports two recommendation scenarios in a unified way, that is, hometown recommendation and out-of-town recommendation. TRM effectively overcomes data sparsity by the complementarity and mutual enhancement of the diverse information associated with users’ check-in activities (e.g., check-in content, time, and location) in the processes of discovering heterogeneous patterns and producing recommendations. To support real-time POI recommendations, we further extend the TRM model to an online learning model, TRM-Online, to track changing user interests and speed up the model training. In addition, based on the learned model, we propose a clustering-based branch and bound algorithm (CBB) to prune the POI search space and facilitate fast retrieval of the top-k recommendations. We conduct extensive experiments to evaluate the performance of our proposals on two real-world datasets, including recommendation effectiveness, overcoming the cold-start problem, recommendation efficiency, and model-training efficiency. The experimental results demonstrate the superiority of our TRM models, especially TRM-Online, compared with state-of-the-art competitive methods, by making more effective and efficient mobile recommendations. In addition, we study the importance of each type of pattern in the two recommendation scenarios, respectively, and find that exploiting temporal patterns is most important for the hometown recommendation scenario, while the semantic patterns play a dominant role in improving the recommendation effectiveness for out-of-town users.

Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder the performance of collaborative filtering-based methods. Moreover, user preferences may vary dramatically with respect to the geographical regions due to different urban compositions and cultures. To address these challenges, we stand on recent advances in deep learning and propose a Spatial-Aware Hierarchical Collaborative Deep Learning model (SH-CDL). The model jointly performs deep representation learning for POIs from heterogeneous features and hierarchically additive representation learning for spatial-aware personal preferences. To combat data sparsity in spatial-aware user preference modeling, both the collective preferences of the public in a given target region and the personal preferences of the user in adjacent regions are exploited in the form of social regularization and spatial smoothing. To deal with the multimodal heterogeneous features of the POIs, we introduce a late feature fusion strategy into our SH-CDL model. The extensive experimental analysis shows that our proposed model outperforms the state-of-the-art recommendation models, especially in out-of-town and cold-start recommendation scenarios.

ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user’s home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd’s preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.

Exploiting Spatio-Temporal User Behaviors for User Linkage

Cross-device and cross-domain user linkage have been attracting a lot of attention recently. An important branch of the study is to achieve user linkage with spatio-temporal data generated by the ubiquitous GPS-enabled devices. The main task in this problem is twofold, i.e., how to extract the representative features of a user; how to measure the similarities between users with the extracted features. To tackle the problem, we propose a novel model STUL (Spatio-Temporal User Linkage) that consists of the following two components. 1) Extract users - spatial features with a density based clustering method, and extract the users - temporal features with the Gaussian Mixture Model. To link user pairs more precisely, we assign different weights to the extracted features, by lightening the common features and highlighting the discriminative features. 2) Propose novel approaches to measure the similarities between users based on the extracted features, and return the pair-wise users with similarity scores higher than a predefined threshold. We have conducted extensive experiments on three real-world datasets, and the results demonstrate the superiority of our proposed STUL over the state-of-the-art methods.

Streaming Ranking Based Recommender Systems

Studying recommender systems under streaming scenarios has become increasingly important because real-world applications produce data continuously and rapidly. However, most existing recommender systems today are designed in the context of an offline setting. Compared with the traditional recommender systems, large-volume and high-velocity are posing severe challenges for streaming recommender systems. In this paper, we investigate the problem of streaming recommendations being subject to higher input rates than they can immediately process with their available system resources (i.e., CPU and memory). In particular, we provide a principled framework called as SPMF (Stream-centered Probabilistic Matrix Factorization model), based on BPR (Bayesian Personalized Ranking) optimization framework, for performing efficient ranking based recommendations in stream settings. Experiments on three real-world datasets illustrate the superiority of SPMF in online recommendations.

An empirical study on user-topic rating based collaborative filtering methods

User based collaborative filtering (CF) has been successfully applied into recommender system for years. The main idea of user based CF is to discover communities of users sharing similar interests, thus, in which, the measurement of user similarity is the foundation of CF. However, existing user based CF methods suffer from data sparsity, which means the user-item matrix is often too sparse to get ideal outcome in recommender systems. One possible way to alleviate this problem is to bring new data sources into user based CF. Thanks to the rapid development of social annotation systems, we turn to using tags as new sources. In these approaches, user-topic rating based CF is proposed to extract topics from tags using different topic model methods, based on which we compute the similarities between users by measuring their preferences on topics. In this paper, we conduct comparisons between three user-topic rating based CF methods, using PLSA, Hierarchical Clustering and LDA. All these three methods calculate user-topic preferences according to their ratings of items and topic weights. We conduct the experiments using the MovieLens dataset. The experimental results show that LDA based user-topic rating CF and Hierarchical Clustering outperforms the traditional user based CF in recommending accuracy, while the PLSA based user-topic rating CF performs worse than the traditional user based CF.

Restricted Boltzmann Machine Based Active Learning for Sparse Recommendation

In recommender systems, users’ preferences are expressed as ratings (either explicit or implicit) for items. In general, more ratings associated with users or items are elicited, more effective the recommendations are. However, almost all user rating datasets are sparse in the real-world applications. To acquire more ratings, the active learning based methods have been used to selectively choose the items (called interview items) to ask users for rating, inspired by that the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount of information about the user’s tastes. Nevertheless, existing active learning based methods, including both static methods and decision-tree based methods, encounter the following limitations. First, the interview item set is predefined in the static methods, and they do not consider the user’s responses when asking the next question in the interview process. Second, the interview item set in the decision tree based methods is very small (i.e., usually less than 50 items), which leads to that the interview items cannot fully reflect or capture the diverse user interests, and most items do not have the opportunity to obtain additional ratings. Moreover, these decision tree based methods tend to choose popular items as the interview items instead of items with sparse ratings (i.e., sparse items), resulting in “Harry Potter Effect” (http://bickson.blogspot.com.au/2012/09/harry-potter-effect-on-recommendations.html). To address these limitations, we propose a new active learning framework based on RBM (Restricted Boltzmann Machines) to add ratings for sparse recommendation in this paper. The superiority of this method is demonstrated on two publicly available real-life datasets.

Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system

Abstract With the rapid prevalence of smart mobile devices and the dramatic proliferation of mobile applications (Apps), App recommendation becomes an emergent task that will benefit different stockholders of mobile App ecosystems. However, the extreme sparsity of user-App matrix and many newly emerging Apps create severe challenges, causing CF-based methods to degrade significantly in their recommendation performance. Besides, unlike traditional items, Apps have rights to access users’ personal resources (e.g., location, message and contact) which may lead to security risk or privacy leak. Thus, users’ choosing of Apps are influenced by not only their personal interests but also their privacy preferences. Moreover, user privacy preferences vary with App categories. In light of the above challenges, we propose a mobile sparse additive generative model (Mobi-SAGE) to recommend Apps by considering both user interests and category-aware user privacy preferences in this paper. To overcome the challenges from data sparsity and cold start, Mobi-SAGE exploits both textual and visual content associated with Apps to learn multi-view topics for user interest modeling. We collected a large-scale and real-world dataset from 360 App store - the biggest Android App platform in China, and conducted extensive experiments on it. The experimental results demonstrate that our Mobi-SAGE consistently and significantly outperforms the other existing state-of-the-art methods, which implies the importance of exploiting category-aware user privacy preferences and the multi-modal App content data on personalized App recommendation.

EISA: an efficient information theoretical approach to value segmentation in large databases

Value disparity is a widely known problem, that contributes to poor data quality results and raises many issues in data integration tasks. Value disparity, also known as column heterogeneity, occurs when the same entity is represented by disparate values, often within the same column in a database table. A first step in overcoming value disparity is to identify the distinct segments. This is a highly challenging task due to high number of features that define a particular segment as well as the need to undertake value comparisons which can be exponential in large databases. In this paper, we propose an efficient information theoretical approach to value segmentation, namely EISA. EISA not only reduces the number of the relevant features but also compresses the size of the values to be segmented. We have applied our method on three datasets with varying sizes. Our experimental evaluation of the method demonstrates a high level of accuracy with reasonable efficiency.