Answer ranking is one of essential steps in open domain question answering systems. The ranking of the retrieved answers directly affects user satisfaction. This paper proposes a new joint model for answer ranking by leveraging context semantic features, which balances both question-answer similarities and answer ranking scores. A publicly available dataset containing 40,000 Chinese questions and 369,919 corresponding answer passages from Sogou Lab is used for experiments. Evaluation on the joint model shows a Precison@1 of 72.6%, which outperforms the state-of-the-art baseline methods.
With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix factorization methods provide effective models which can be used in POI recommendation. However, there are two main challenges which should be addressed to improve the performance of POI recommendation methods. First, leveraging geographical information to capture both the user’s personal, geographic profile and a location’s geographic popularity. Second, incorporating the geographical model into the matrix factorization approaches. To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users’ and locations’ points of view. To this end, an effective geographical model is proposed by considering the user’s main region of activity and the relevance of each location within that region. Then, the proposed local geographical model is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation. Experimental results on two well-known datasets demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.
The present study concerns depth-k pooling for building IR test collections. At TREC, pooled documents are traditionally presented in random order to the assessors to avoid judgement bias. In contrast, an approach that has been used widely at NTCIR is to prioritise the pooled documents based on “pseudorelevance,” in the hope of letting assessors quickly form an idea as to what constitutes a relevant document and thereby judge more efficiently and reliably. While the recent TREC 2017 Common Core Track went beyond depth-k pooling and adopted a method for selecting documents to judge dynamically, even this task let the assessors process the usual depth-10 pools first: the idea was to give the assessors a “burn-in” period, which actually appears to echo the view of the NTCIR approach. Our research questions are: (1) Which depth-k ordering strategy enables more efficient assessments? Randomisation, or prioritisation by pseudorelevance? (2) Similarly, which of the two strategies enables higher inter-assessor agreements? Our experiments based on two English web search test collections with multiple sets of graded relevance assessments suggest that randomisation outperforms prioritisation in both respects on average, although the results are statistically inconclusive. We then discuss a plan for a much larger experiment with sufficient statistical power to obtain the final verdict.
Recommender systems (RS) are being used in a broad range of applications, from online shopping websites to music streaming platforms, which aim to provide users high-quality personalized services. Collaborative filtering (CF) is a promising technique to ensure the accuracy of a recommender system, which can be divided into specific tasks such as rating prediction and item ranking. However, there is a larger volume of published works studying the problem of rating prediction, rather than item ranking though it is recognized to be more appropriate for the final recommendation in a real application. On the other hand, many studies on item ranking devoted to leveraging implicit feedback are limited in performance improvements due to the uniformity of implicit feedback. Hence, in this paper, we focus on item ranking with informative explicit feedback, which is also called collaborative ranking. In particular, we propose a novel recommendation model termed context-aware collaborative ranking (CCR), which adopts a logistic loss function to measure the predicted error of ranking and exploits the inherent preference context derived from the explicit feedback. Moreover, we design an elegant strategy to distinguish between positive and negative samples used in the process of model training. Empirical studies on four real-world datasets clearly demonstrate that our CCR outperforms the state-of-the-art methods in terms of various ranking-oriented evaluation metrics.
