Both reviews and user-item interactions have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. In this paper, we propose a novel context-aware user-item representation learning model for rating prediction, named CARL. Namely, CARL derives a joint representation for a given user-item pair based on their individual latent features and latent feature interactions. Then, CARL adopts Factorization Machines to further model higher-order feature interactions on the basis of the user-item pair for rating prediction. Specifically, two separate learning components are devised in CARL to exploit review data and interaction data respectively: review-based feature learning and interaction-based feature learning. In review-based learning component, with convolution operations and attention mechanism, the relevant features for a user-item pair are extracted by jointly considering their corresponding reviews. However, these features are only reivew-driven and may not be comprehensive. Hence, interaction-based learning component further extracts complementary features from interaction data alone, also on the basis of user-item pairs. The final rating score is then derived with a dynamic linear fusion mechanism. Experiments on seven real-world datasets show that CARL achieves significantly better rating predication accuracy than existing state-of-the-art alternatives.
E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well-known that there are two types of preferences: long-term ones and short-term ones. The former refers to users' inherent purchasing bias and evolves slowly. By contrast, the latter reflects users' purchasing inclination in a relatively short period. They both affect users' current purchasing intentions. However, few research efforts have been dedicated to jointly model them for personalized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long- and short-term user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query. This unique design enables our model to capture users' current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for personalized search.
In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g. restaurants, shops) from which they were posted. This explicitly recovers the venue context which is essential for applications such as location-based advertising or user profiling. For this geolocation task, we focus on geolocating tweets which are contained in tweet sequences. In a tweet sequence, tweets are posted from some latent venue(s) by the same user and within a short time interval. This scenario arises from two observations: (1) it is quite common that users post multiple tweets in a short time and (2) most tweets are not geocoded. To more accurately geolocate a tweet, we propose a model that performs query expansion on the tweet (query) using two novel approaches. The first approach temporal query expansion considers users' staying behavior around venues. The second approach visitation query expansion leverages on user revisiting the same or similar venues in the past. We combine both query expansion approaches via a novel fusion framework and overlay them on a Hidden Markov Model to account for sequential information. In our comprehensive experiments across multiple datasets and metrics, we show our proposed model to be more robust and accurate than other baselines.
User interactions can be considered to constitute different feedback channels, e.g., view, click, like or follow, that provide implicit information on users preferences. Each implicit feedback channel typically carries a unary, positive-only signal, which can be exploited by collaborative fltering models to generate lists of personalized recommendations. This paper investigates how a learning-to-rank recommender system can best take advantage of implicit feedback signals from multiple channels. We focus on Factorization Machines (FM) with Bayesian Personalized Ranking (BPR), a pairwise learning-to-rank method, that allows us to experiment with different forms of exploitation. We perform extensive experiments on three datasets with multiple types of feedback to arrive at a series of insights.We compare conventional, direct integration of feedback types with our proposed method that exploits multiple feedback channels during the sampling process of training.We refer our method as multi-channel sampling. Our results show that multi-channel feedback sampling outperforms conventional integration, and that sampling with the relative level" of feedback, is always superior to a level-blind sampling approach.We evaluated our method experimentally on three datasets in different domains and found out that with our multi-channel sampler the accuracy and the item coverage of recommendations can be improved significantly compared to state-of-the-art models.
Personalized rating prediction is an important research problem in recommender systems. Although the latent factor model achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this paper, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply the proposed multi-modal aspect-aware topic model on text reviews and item images to model user's preferences and item's properties from different aspects. Then the learned user preferences and item features are integrated into a novel aspect-aware latent factor model for aspect rating estimation. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset. Results show that (1) our method achieves significant improvement compared to strong baseline methods, especially for users with only few ratings; (2) item visual features can improve the prediction performance - the effects of item image features on improving the prediction results depend on the importance of the visual features for the items; and (3) our model can explicitly interpret the predicted results in great detail.
Auxiliary information such as user reviews or product images has been extensively leveraged in recommender systems. Although it has been verified to be effective in terms of enhancing the recommendation performance, most state-of-the-art models have to embed complicated models in their framework to tame such unstructured data, which limits the model runtime efficiency. To solve this problem, in this paper, we decompose the modeling of auxiliary information and user-item interactions by a generalized distillation framework, where in the training phase, we leverage a powerful teacher model for auxiliary information modeling to teach a simple collaborative filtering (CF) student model, and then only this succinct yet enhanced student model is used to make fast predictions at test time. Specifically, we take user reviews as the auxiliary information, and according to their specific characters, we propose a Selective Distillation Network (SDNet) to leverage textual information in a more effective manner. Extensive experiments verify that our model can not only improve the performance of rating prediction, but also can significantly reduce the time consumption when making predictions as compared with several state-of-the-art methods.
Faceted search is quickly becoming a common feature on most search interfaces in e-commerce websites, digital libraries, government's open information portals, etc. Beyond the existing studies on developing algorithms for faceted search and empirical studies on facet usage, this study investigated user real-time interactions with facets over the course of a search from both data science and human factor perspectives. It adopted a Random Forest (RF) model to successfully predict facet use using search dynamic variables. In addition, the RF model provided a ranking of variables by their predictive power, which suggests that the search process follows rhythmic flow of a sequence within which facet addition is mostly influenced by its immediately preceding action. In the follow-up user study, we found that participants used facets at critical points from the beginning to end of search sessions. Participants used facets for distinctive reasons at different stages. They also used facets implicitly without applying the facets to their search. Most participants liked the faceted search, although a few participants were concerned about the choice overload introduced by facets. The results of this research can be used to understand information seekers and propose or refine a set of practical design guidelines for faceted search.
Heterogeneous one-class collaborative filtering (HOCCF) is an emerging and important problem in recommender systems, where two different types of one-class feedback, i.e., purchases and browses, are available as input data. The associated challenges include ambiguity of browses, scarcity of purchases, and heterogeneity arising from different feedback. In this paper, we propose to model purchases and browses from a new perspective, i.e., users' roles of mixer, browser and purchaser. Specifically, we design a novel transfer learning solution termed role-based transfer to rank (RoToR), which contains two variants, i.e., integrative RoToR and sequential RoToR. In integrative RoToR, we leverage browses into the preference learning task of purchases, in which we take each user as a sophisticated customer (i.e., mixer) that is able to take different types of feedback into consideration. In sequential RoToR, we aim to simplify the integrative one by decomposing it into two dependent phases according to a typical shopping process. Furthermore, we instantiate both variants using different preference learning paradigms such as pointwise preference learning and pairwise preference learning. Finally, we conduct extensive empirical studies with various baseline methods on two large public datasets and find that our RoToR can perform significantly more accurate than the state-of-the-art methods.
With the availability of abundant online multi-relational video information, recommender systems that can effectively exploit these sorts of data and suggest creatively interesting items will become increasingly important. Recent research illustrates that tensor models offer effective approaches for complex multi-relational data learning and missing element completion. So far, most tensor-based clustering has focused on accuracy. Given the dynamic nature of online media, recommendation in this setting is more challenging, as it is difficult to capture the users' dynamic topic distributions in sparse data settings. Targeting at constructing a recommender system that can compromise between accuracy and creativity, a deep Bayesian probabilistic tensor framework for tag and item recommendation is proposed. Based on the Canonical PARAFAC (CP) decomposition, a Bayesian multi-layer factorization is imposed on the mode factor matrix to find a more compact representation. During the score ranking processes, a metric called Bayesian surprise is incorporated to increase the creativity of the recommended candidates. The new algorithm is evaluated on both synthetic and large-scale real-world problems. An empirical study for video recommendation demonstrates the superiority of the proposed model and indicates that it can better capture the latent patterns of interactions and generates interesting recommendations based on creative tag combinations.
Sentence-based summarization aims at extracting concise summaries of collections of textual documents.The most effective multilingual strategies rely on Latent Semantic Analysis (LSA) and frequent itemset mining, respectively.LSA-based summarizers pick the document sentences that cover the most important concepts. Concepts are modeled as combinations of single document terms and they are derived from a term-by-sentence matrix by exploiting the Singular Value Decomposition (SVD). Itemset-based summarizers pick the sentences that contain the largest number of frequent itemsets, which represent combinations of frequently co-occurring terms.The main drawbacks of existing approaches are (i) the inability of LSA to consider the correlation between combinations of multiple document terms with the underlying concepts, and (ii) the inability of itemset-based summarizers to correlate itemsets with the underlying document concepts. To overcome the issues of both the aforesaid algorithms, we propose a new summarization approach that exploits frequent itemsets to describe all the latent concepts covered by the documents under analysis and LSA to reduce the potentially redundant set of itemsets to a compact set of uncorrelated concepts. The summarizer selects the sentences that cover the latent concepts with minimal redundancy. We tested the summarization algorithm on both multilingual and English-written benchmark document collections.
The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are two most important contextual factors in the user?s decision making for choosing a POI to visit. In this paper, we focus on the spatiotemporal context-aware POI recommendation which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a spatiotemporal context-aware & translation-based recommender framework (STA) to model the third-order relationship among users, POIs and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a ?transition space? where spatiotemporal contexts (i.e., a
Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted.