| 文献作者 | Alexander H. Miller | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2018-10-10 | ||||||||
| 文献关键字 | Key-Value Memory Model, End-to-End, softmax | ||||||||||
| 摘要描述 | Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven effective. Unfortunately KBs often suffer from being too restrictive, as the schema cannot support certain types of answers, and too sparse, e.g. Wikipedia contains much more information than Freebase. In this work we introduce a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation. To compare using KBs, information extraction or Wikipedia documents directly in a single framework we construct an analysis tool, WIKIMOVIES, a QA dataset that contains raw text alongside a preprocessed KB, in the domain of movies. Our method reduces the gap between all three settings. It also achieves state-of-the-art results on the existing WIKIQA benchmark. | ||||||||||
| 文献作者 | Tuan-Anh Nguyen Pham | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2018-10-05 | ||||||||
| 文献关键字 | Location-based social networks; region recommendation; out- of-town recommendation | ||||||||||
| 摘要描述 | With the rapid growth of location-based social networks (LBSNs), it is now available to analyze and understand user mobility behavior in real world. Studies show that users usually visit nearby points of interest (POIs), located in small regions, especially when they travel out of their hometowns. However, previous out-of-town recommendation systems mainly focus on recommending individual POIs that may reside far from each other, which makes the recommendation results less useful. In this paper, we introduce a novel problem called Region Recommendation, which aims to recommend an out-of-town region of POIs that are likely to be visited by a user. The proximity characteristic of user mobility behavior implies that the probability of visiting one POI depends on those of nearby POIs. Thus, to make accurate region recommendation, our proposed model exploits the influence between POIs, instead of treating them individually. Moreover, to overcome the efficiency problem of searching the best region, we propose a sweeping line-based method, and subsequently a constant-bounded algorithm for better efficiency. Experiments on two real-world datasets demonstrate the improved effectiveness of our models over baseline methods and efficiency of the approximate algorithm. | ||||||||||
| 文献作者 | Suhang Wang | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2018-10-05 | ||||||||
| 文献关键字 | POI as item, image as content of item, PMF, CNN, | ||||||||||
| 摘要描述 | The rapid growth of Location-based Social Networks (LB- SNs) provides a vast amount of check-in data, which facili- tates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, i.e., temporal patterns, geographi- cal influence, social correlations and textual content indica- tions. For example, user’s visits to locations have temporal patterns and users are likely to visit POIs near them. In real-world LBSNs such as Instagram, users can upload pho- tos associating with locations. Photos not only reflect users’ interests but also provide informative descriptions about lo- cations. For example, a user who posts many architecture photos is more likely to visit famous landmarks; while a user posts lots of images about food has more incentive to visit restaurants. Thus, images have potentials to improve the performance of POI recommendation. However, little work exists for POI recommendation by exploiting images. In this paper, we study the problem of enhancing POI recom- mendation with visual contents. In particular, we propose a new framework Visual Content Enhanced POI recommenda- tion (VPOI), which incorporates visual contents for POI rec- ommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. | ||||||||||
| 文献作者 | Buru Chang | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2018-10-05 | ||||||||
| 文献关键字 | Embedding model, Sequential information + content of POI | ||||||||||
| 摘要描述 | Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommenda- tion models based on conventional sequential-data modeling approaches have been proposed. How- ever, such models focus on only a user’s check- in sequence information and the physical distance between POIs. Furthermore, they do not utilize the characteristics of POIs or the relationships be- tween POIs. To address this problem, we pro- pose CAPE, the first content-aware POI embedding model which utilizes text content that provides in- formation about the characteristics of a POI. CAPE consists of a check-in context layer and a text con- tent layer. The check-in context layer captures the geographical influence of POIs from the check-in sequence of a user, while the text content layer cap- tures the characteristics of POIs from the text con- tent. To validate the efficacy of CAPE, we con- structed a large-scale POI dataset. In the experi- mental evaluation, we show that the performance of the existing POI recommendation models can be significantly improved by simply applying CAPE to the models. | ||||||||||
| 文献作者 | Qiang Liu | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2018-10-04 | ||||||||
| 文献关键字 | RNN理解; Sequential information add Contextual information | ||||||||||
| 摘要描述 | Since sequential information plays an importan- t role in modeling user behaviors, various sequential rec- ommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real- world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Net- works (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adap- tive context-specific transition matrices. The adaptive context- specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA- RNN model yields significant improvements over state-of-the- art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset. | ||||||||||
| 文献作者 | Shanshan Feng, Gao Cong, Bo An, Yeow Meng Chee | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2018-10-03 | ||||||||
| 文献关键字 | Future Visitor Prediction, Sequential Recommendation, binary tree, Probability Estimation | ||||||||||
| 摘要描述 | With the increasing popularity of location-aware social me- dia applications, Point-of-Interest (POI) recommendation has recently been extensively studied. However, most of the exist- ing studies explore from the users’ perspective, namely rec- ommending POIs for users. In contrast, we consider a new re- search problem of predicting users who will visit a given POI in a given future period. The challenge of the problem lies in the difficulty to effectively learn POI sequential transition and user preference, and integrate them for prediction. In this work, we propose a new latent representation model POI2Vec that is able to incorporate the geographical influence, which has been shown to be very important in modeling user mobil- ity behavior. Note that existing representation models fail to incorporate the geographical influence. We further propose a method to jointly model the user preference and POI sequen- tial transition influence for predicting potential visitors for a given POI. We conduct experiments on 2 real-world datasets to demonstrate the superiority of our proposed approach over the state-of-the-art algorithms for both next POI prediction and future user prediction. | ||||||||||
| 文献作者 | Haochao Ying; Hui Xiong | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2018-10-03 | ||||||||
| 文献关键字 | Sequential Recommendation, Long-term, Short-term, Dynamic, Next recommendation, Small, Gowalla, one day a session | ||||||||||
| 摘要描述 | With a large amount of user activity data accumu- lated, it is crucial to exploit user sequential behav- ior for sequential recommendations. Convention- ally, user general taste and recent demand are com- bined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic char- acters. Moreover, they integrate user-item or item- item interactions through a linear way which lim- its the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above proper- ties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones. | ||||||||||
| 文献作者 | Liang Hu; Jian Cao | ||||||||||
| 文献发表年限 | 2013 | 创建时间 | 2018-08-28 | ||||||||
| 文献关键字 | Recommender System, Cross-Domain Collaborative Filtering, Triadic Factorization; 张量分解 | ||||||||||
| 摘要描述 | Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users’ uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user- item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models. | ||||||||||
| 文献作者 | Felicia Brittman | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2018-07-14 | ||||||||
| 文献关键字 | 写作指导；老外总结的中国人写英语文章最常犯的错误总结 | ||||||||||
| 摘要描述 | This paper presents some of the most common Chinese-English habits observed from over two hundred English technical papers by Chinese writers. The habits are explained and in most cases, example text from an actual paper is given along with preferred text. An attempt is made to explain how to correct and prevent such mistakes. In some cases, a possible explanation of why the habit occurs is also given. This paper can serve as an individual guide to editing technical papers especially when a native English-speaking editor is unavailable. | ||||||||||
