Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage location's spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data; Second, it is strongly connected with widely-used neighbor additive models and graph Laplacian regularized models. We finally evaluate GeoMF++ on two large-scale LBSN datasets with respect to both warm-start and cold-start scenarios. The experimental results show that GeoMF++ consistently outperforms the state-of-the-arts and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space.
In this paper, we take the initiative to study the learning behavioral characteristics of users on online judge systems, which are open, competitive, and self-regulated. We propose to automatically mine subject and difficulty information for problems based on users' learning trace data. An OJ system maintains a large pool of problems related to some specific domain, typically organized by volume. By performing careful data analysis, we have observed two major learning modes (i.e., patterns) of users on OJ systems with the volume organization, namely volume-consecutive and subject-consecutive. It has been found that although the problems from the same subject are distributed across multiple volumes, the users are indeed able to spontaneously identify problems from the same subject for practice. Our observation can find relevance and support in classic educational psychology. To capture the two learning modes, we propose a novel two-mode Markov topic model. For estimating the difficulty of problems, we further propose a subject-aware competition-based expertise model based on the learned topic information. Extensive experiments on three large online judge datasets have demonstrated the effectiveness of our approach in three different tasks, including skill topic extraction, expertise competition prediction and problem recommendation
Many social network applications depend on robust representations of spatio-temporal data. In this work, we present an embedding model based on feed-forward neural networks which transforms social media check-ins into dense feature vectors encoding geographic, temporal, and functional aspects for modelling places, neighborhoods, and users. We employ the embedding model in a variety of applications including location recommendation, urban functional zone study, and crime prediction. For location recommendation, we propose a Spatio-Temporal Embedding Similarity algorithm (STES) based on the embedding model. In a range of experiments on real life data collected from Foursquare, we demonstrate our models effectiveness at characterizing places and people and its efficacy in aforementioned problem domains. Finally, we select eight major cities around the globe and verify the robustness and generality of our model by porting pre-trained models from one city to another, and thereby alleviating the need for costly local training.
When content consumers explicitly judge content positively, we consider them to be engaged. Unfortunately, explicit user evaluations are difficult to collect, as they require user effort. Therefore, we propose to use device interactions as implicit feedback to detect engagement. We assess the usefulness of swipe interactions on tablets for predicting engagement, and make the comparison with using traditional features based on time spent. We gathered two unique datasets of more than 250,000 swipes, 100,000 unique article visits, and over 35,000 explicitly judged news articles, by modifying two commonly used tablet apps of two newspapers. We tracked all device interactions of 407 experiment participants during one month of habitual news reading. We employed a behavioral metric as a proxy for engagement, because our analysis needed to be scalable to many users, and scanning behavior required us to allow users to indicate engagement quickly. We point out the importance of taking into account content ordering, report the most predictive features, zoom in on briefly read content and on the most frequently read articles. Our findings demonstrate that fine-grained tablet interactions are useful indicators of engagement for newsreaders on tablets. The best features successfully combine both time-based aspects and swipe interactions.
Event-based social networks (EBSNs) are becoming popular in recent years. Users can publish a planned event on an EBSN website, calling for other users to participate the event. When a user is making a decision on whether to participate an event in EBSNs, one aspect for consideration is who else have agreed to join this event. Existing participants of the event may affect the decision of the user, to which we refer as participant influence. However, participant influence is not well studied by previous works. In this paper, we propose a Poisson factorization model to build an event recommendation model which considers participant influence and exploits the influence of existing participant, on the decisions of new participants. The effect of participant influence is associated with the target event, the host group of the event, and the location of the event. Furthermore, our proposed model can extract latent event topics from event text descriptions, and characterize events, groups, and locations by distributions of event topics. Associations between latent event topics and participation influence are exploited for improving event recommendation. We have conducted extensive experiments on some datasets extracted from a real-world EBSN. The results demonstrate that the consideration of participant influence can improve event recommendation.
The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries rather than type them. While this kind of voice search is still in its infancy, it is gradually becoming more widespread. In this paper, we report a comprehensive voice search query log analysis of a commercial web search engines mobile application. We compare voice and text search by various aspects, with special focus on the semantic and syntactic characteristics of the queries. Our analysis suggests that voice queries focus more on audio-visual content and question answering and less on social networking and adult domains. In addition, voice queries are more commonly submitted on the go. We also conduct an empirical evaluation showing that the language of voice queries is closer to natural language than the language of text queries. Our analysis points out further differences between voice and text search. We discuss the implications of these differences for the design of future voice-enabled web search tools.
Web search engines present, for some queries, a cluster of results from the same specialized domain (vertical) on the search results page (SERP). We introduce a comprehensive analysis of the presentation of such clusters from seven different verticals, based on the logs of a commercial web search engine. This analysis reveals several unique characteristics, such as size, rank, and clicks, of result clusters from community question and answering websites. The study of properties of this result cluster, specifically, as part of the SERP, has received little attention in previous work. Our analysis also motivates the pursuit of a long-standing challenge in ad hoc retrieval, namely selective cluster retrieval. In our setting, the specific challenge is to select for presentation the documents most highly ranked either by a cluster-based approach (namely, those in the top-retrieved cluster) or by a document-based approach. We address this classification task by representing queries with features based on those utilized for ranking the clusters, query performance predictors, and properties of the document clustering structure. Empirical evaluation performed with TREC data shows that our approach outperforms a recently proposed state-of-the-art cluster-based document retrieval method, as well as state-of-the-art document retrieval methods which do not account for inter-document similarities.
User-generated trajectories (UGT), such as travel records from bus companies, capture rich information of human mobility in the offline world. However, some interesting applications of these raw footprints have not been well exploited due to the lack of textual information to infer the subject's personal interests. Although there are rich semantic information contained in the user-generated contents (STUGC) published in the online world, such as Twitter, less effort has been made to utilize these information to facilitate the interest discovery process. In this paper, we design an effective probabilistic framework named CO2 to connect the offline world with the online world in order to discover the users' interests directly from their raw footprints in UGT. CO2 first infers the trip intentions by utilizing the semantic information in STUGC, and then discovers the user interests by aggregating the intentions. To evaluate the effectiveness of CO2, we use two large-scale real world datasets as a case study and further conduct a questionnaire survey to show the superior performance of CO2. In our preliminary study, we briefly introduce the overall idea of CO2. In this paper, we present a thorough analysis of CO2 for the first time, including the model inference procedure, the complexity analysis, the related work and the extensive experiments.
Quote is a language phenomenon of transcribing the saying of someone else, like a proverb or a famous saying. Proper usage of quote can usually make the statement more elegant and convincing. However, sometimes we may be so eager to use a quote somewhere, but have no idea about the relevant quotes to express our idea. Therefore it is exciting to have a recommendation system to recommend potential quotes for use while writing. This paper proposes a novel task of quote recommendation, and present a quote recommendation system called QuoteRec. Two models are proposed to learn the vector representations of quotes and contexts, and then rank the candidate quotes according to the representations. The first model learns the quote representation according to the contexts of a quote. The second model is a neural network model, which learns the representation of a quote by concerning both its content and contexts. Moreover, in the second model a mechanism is introduced to model the subtle usage of word meaning in the quote. Experimental results show that the proposed models are quite effective at learning the semantic representations of quotes, and the neural network model achieves state-of-the-art results on the quote recommendation task.
This paper presents a framework for speedy video matching and retrieval through detection and measurement of visual similarity. The frameworks efficiency stems from its power to encode a given shot content into compact fixed length signature that facilitates towards a robust real-time matching. Separate scene and motion signatures are developed and fused together to fully represent and match respective video shots. The framework works on thumbnail images (DC-image from the MPEG stream). Scene information is captured through the Statistical Dominant Colour Profile (SDCP), while motion information is captured through a graph-based signature, called the Dominant Colour Graph Profile (DCGP). The SDCP is a fixed-length compact signature that statistically encodes the colours spatio-temporal patterns across video frames. The DCGP is a fixed-length signature that records and tracks blocks movement across video frames, where the graph structural properties are used to extract the signature values. Finally, the overall video signature is generated by fusing the individual scene and motion signatures, where the matching is done by directly comparing the respective fused video signatures. The signature-based aspect of the proposed framework is the key to its high matching speed (>2000 fps), compared to the current techniques that relies on exhaustive processing, such as dense sampling.