We present our solution to the Yandex Personalized Web Search Challenge. The aim of this challenge was to use the historical search logs to personalize top-N document rankings for a set of test users. We used over 100 features extracted from userand query-depended contexts to train neural net and tree-based learning-to-rank and regression models. Our final submission, which was a blend of sever...

Most existing learning to rank based summarization methods only used content relevance of sentences with respect to queries to rank or estimate sentences, while neglecting sentence relationships. In our work, we propose a novel model, RelationListwise, by integrating relation information among all the estimated sentences into listMLE-Top K, a basic listwise learning to rank model, to improve th...

Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a learning-to-rank formulation that optimizes the fraction of satisfied users, with several scalable a...

Most Twitter search systems generally treat a tweet as a plain text when modeling relevance. However, a series of conventions allows users to tweet in structural ways using combination of different blocks of texts. These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and sequence of these blocks captures changing discourse. Previ...

This study investigated the accuracy and completeness of mental models users have of Web search engines in the context of a comparison of matched data obtained from samples from 2000 and 2010. The performance measures time, steps and accuracy were assessed along with 17 salient features of Web search engines identified in the study conducted in 2000. The results indicated that the 2010 sample h...

ListNet is a well-known listwise learning to rank model and has gained much attention in recent years. A particular problem of ListNet, however, is the high computation complexity in model training, mainly due to the large number of object permutations involved in computing the gradients. This paper proposes a stochastic ListNet approach which computes the gradient within a bounded permutation ...

This paper describes Peking University’s approach to the Ad Hoc Track. In our first participation, results for all four tasks were submitted: the Best In Context, the Focused, the Relevance In Context and the Thorough. Based on retrieval method Okapi BM25, we implement two different ranking methods NormalBM25 and LearningBM25 according to different parameter settings. Specially, the parameters ...

The aim of optimising information retrieval (IR) systems using a risksensitive evaluation methodology is to minimise the risk of performing any particular topic less effectively than a given baseline system. Baseline systems in this context determine the reference effectiveness for topics, relative to which the effectiveness of a given IR system in minimising the risk will be measured. However,...

Selecting the right drugs for the right patients is a primary goal of precision medicine. In this manuscript, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to accurately predicting 1). the ranking positions of sensitive drugs and 2). the ranking orders among sensitive drugs in cancer cell lines based on ...

In this paper, we argue that only using behavioural motion information, we are able to predict the interest of observers when looking at face-to-face interactions. We propose a set of movement-related features from body, face, and mouth activity in order to define a set of higher level interaction features, such as stress, activity, speaking engagement, and corporal engagement. Error-Correcting...