scholarly journals Improving Bi-encoder Document Ranking Models with Two Rankers and Multi-teacher Distillation

Author(s):  
Jaekeol Choi ◽  
Euna Jung ◽  
Jangwon Suh ◽  
Wonjong Rhee
Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 39
Author(s):  
Zhenyang Li ◽  
Guangluan Xu ◽  
Xiao Liang ◽  
Feng Li ◽  
Lei Wang ◽  
...  

In recent years, entity-based ranking models have led to exciting breakthroughs in the research of information retrieval. Compared with traditional retrieval models, entity-based representation enables a better understanding of queries and documents. However, the existing entity-based models neglect the importance of entities in a document. This paper attempts to explore the effects of the importance of entities in a document. Specifically, the dataset analysis is conducted which verifies the correlation between the importance of entities in a document and document ranking. Then, this paper enhances two entity-based models—toy model and Explicit Semantic Ranking model (ESR)—by considering the importance of entities. In contrast to the existing models, the enhanced models assign the weights of entities according to their importance. Experimental results show that the enhanced toy model and ESR can outperform the two baselines by as much as 4.57% and 2.74% on NDCG@20 respectively, and further experiments reveal that the strength of the enhanced models is more evident on long queries and the queries where ESR fails, confirming the effectiveness of taking the importance of entities into account.


2016 ◽  
Vol 42 (6) ◽  
pp. 725-747 ◽  
Author(s):  
Bilel Moulahi ◽  
Lynda Tamine ◽  
Sadok Ben Yahia

With the advent of Web search and the large amount of data published on the Web sphere, a tremendous amount of documents become strongly time-dependent. In this respect, the time dimension has been extensively exploited as a highly important relevance criterion to improve the retrieval effectiveness of document ranking models. Thus, a compelling research interest is going on the temporal information retrieval realm, which gives rise to several temporal search applications. In this article, we intend to provide a scrutinizing overview of time-aware information retrieval models. We specifically put the focus on the use of timeliness and its impact on the global value of relevance as well as on the retrieval effectiveness. First, we attempt to motivate the importance of temporal signals, whenever combined with other relevance features, in accounting for document relevance. Then, we review the relevant studies standing at the crossroads of both information retrieval and time according to three common information retrieval aspects: the query level, the document content level and the document ranking model level. We organize the related temporal-based approaches around specific information retrieval tasks and regarding the task at hand, we emphasize the importance of results presentation and particularly timelines to the end user. We also report a set of relevant research trends and avenues that can be explored in the future.


2011 ◽  
Vol 37 (6) ◽  
pp. 614-636 ◽  
Author(s):  
Mariam Daoud ◽  
Lynda Tamine ◽  
Mohand Boughanem

The goal of search personalization is to tailor search results to individual users by taking into account their profiles, which include their particular interests and preferences. As these latter are multiple and change over time, personalization becomes effective when the search process takes into account the current user interest. This article presents a search personalization approach that models a semantic user profile and focuses on a personalized document ranking model based on an extended graph-based distance measure. Documents and user profiles are both represented by graphs of concepts issued from predefined web ontology, namely, the Open Directory Project directory (ODP). Personalization is then based on reordering the search results of related queries according to a graph-based document ranking model. This former is based on using a graph-based distance measure combining the minimum common supergraph and the maximum common subgraph between the document and the user profile graphs. We extend this measure in order to take into account a semantic recovery at exact and approximate concept-level matching. Experimental results show the effectiveness of our personalized graph-based ranking model compared with Yahoo and different personalized ranking models performed using classical graph-based measures or vector-space similarity measures.


2020 ◽  
Vol 11 (5) ◽  
pp. 1-24
Author(s):  
Jizhou Huang ◽  
Haifeng Wang ◽  
Wei Zhang ◽  
Ting Liu

Author(s):  
Chen Lin ◽  
Xiaolin Shen ◽  
Si Chen ◽  
Muhua Zhu ◽  
Yanghua Xiao

The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i.e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models.Our general assumptions are (1) there are K universal hidden aspects. In each evaluation session, only one aspect is chosen as the prominent aspect according to user preference. (2) Evaluations over prominent and non-prominent aspects are non-compensatory. Evaluation is mainly based on item performance on the prominent aspect. For non-prominent aspects the user sets a minimal acceptable threshold. We give a conceptual model for these general assumptions. We show how this conceptual model can be realized in both pointwise rating prediction models and pair-wise ranking prediction models. Experiments on real-world data sets validate that adopting non-compensatory rules improves recommendation performance for both rating and ranking models.


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