Bo-Wen Zhang

Social Book Search Reranking with Generalized Content-Based Filtering

Semantically searching and navigating products (e.g., on Taobao.com or Amazon.com) with professional metadata and user-generated content from social media is a hot topic in information retrieval and recommendation systems, while most existing methods are specifically designed as a purely searching system. In this paper, taking Social Book Search as an example, we propose a general search-recommendation hybrid system for this topic. Firstly, we propose a Generalized Content-Based Filtering (GCF) model. In this model, a preference value, which flexibly ranges from 0 to 1, is defined to describe a users preference for each item to be recommended, unlike conventionally using a set of preferable items. We also design a weighting formulation for the measure of recommendation. Next, assuming that the query in a searching system acts as a user in a recommendation system, a general reranking model is constructed with GCF to rerank the initial resulting list by utilizing a variety of rich social information. Afterwards, we propose a general search-recommendation hybrid framework for Social Book Search, where learning-to-rank is used to adaptively combine all reranking results. Finally, our proposed system is extensively evaluated on the INEX 2012 and 2013 Social Book Search datasets, and has the best performance (NDCG@10) on both datasets compared to other state-of-the-art systems. Moreover, our system recently won the INEX 2014 Social Book Search Evaluation.

ISART: A Generic Framework for Searching Books with Social Information

Effective book search has been discussed for decades and is still future-proof in areas as diverse as computer science, informatics, e-commerce and even culture and arts. A variety of social information contents (e.g, ratings, tags and reviews) emerge with the huge number of books on the Web, but how they are utilized for searching and finding books is seldom investigated. Here we develop an Integrated Search And Recommendation Technology (IsArt), which breaks new ground by providing a generic framework for searching books with rich social information. IsArt comprises a search engine to rank books with book contents and professional metadata, a Generalized Content-based Filtering model to thereafter rerank books with user-generated social contents, and a learning-to-rank technique to finally combine a wide range of diverse reranking results. Experiments show that this technology permits embedding social information to promote book search effectiveness, and IsArt, by making use of it, has the best performance on CLEF/INEX Social Book Search Evaluation datasets of all 4 years (from 2011 to 2014), compared with some other state-of-the-art methods.

LSTM\(^{2}\): Multi-Label Ranking for Document Classification

Multi-label document classification is a typical challenge in many real-world applications. Multi-label ranking is a common approach, while existing studies usually disregard the effects of context and the relationships among labels during the scoring process. In this paper, we propose an Long Short Term Memory (LSTM)-based multi-label ranking model for document classification, namely LSTM

A Generic Framework for Biomedical Snippet Retrieval

^2

TED-KISS: A Known-Item Speech Video Search Benchmark

2 consisting of repLSTM—an adaptive data representation process and rankLSTM—a unified learning-ranking process. In repLSTM, the supervised LSTM is used to learn document representation by incorporating the document labels. In rankLSTM, the order of the documents labels is rearranged in accordance with a semantic tree, in which the semantics are compatible with and appropriate to the sequential learning of LSTM. The model can be wholly trained by sequentially predicting labels. Connectionist Temporal Classification is performed in rankLSTM to address the error propagation for a variable number of labels in each document. Moreover, a variety of experiments with document classification conducted on three typical datasets reveal the impressive performance of our proposed approach.

Biomedical literature classification with a CNNs-based hybrid learning network

During every summer holidays, several editions of reading lists are recommended and emerged on mass media, e.g., New York Times, and BBC. However, these reading lists are built for whole people with general topics for some purposes. What if we expect the books of a specific topic at a specific moment? How to generate the requested reading list for our own automatically? In this paper, we propose a searching framework for building a topical reading list anytime, where the Relevance (between topics and books), Quality (of books), Timeliness (of popularities) and Diversity (of results) are embedded into vector representations respectively based on user-generated contents and statistics on social media. We collected 8,197 real-world topics from 198 diverse groups on Librarything.com. The proposed methods are evaluated on the topic collection and the public benchmarks Social Book Search 2012-2016 (SBS). Experimental results demonstrate the robustness and effectiveness of our framework.

A calculation method for social network user credibility

In the biomedicine domain, a large number of papers are published every day, which is crucial to search for the relevant answers for the users query. However, documents are not exactly what the users want. Instead, snippets, small segments from the documents, are more proper to meet the requirement of the users. Hence, this paper proposes a biomedical snippet retrieval framework to exactly locate the answers (snippets) for the biomedical questions (queries) from the users. In our framework, word embeddings with biomedical meanings are trained for query expansion. Then, the expansion query is used to retrieve the relevant snippets from the candidate documents using the sequential dependence model which can benefit retrieval results. Finally, our proposed framework is evaluated on the BioASQ 2014 task datasets, and has the best performance (MAP@100) compared to state-of-the-art systems. Moreover, our framework has competitive results on the BioASQ 2015 task.

A Multi-strategy Query Processing Approach for Biomedical Question Answering: USTB_PRIR at BioASQ 2017 Task 5B.

Massive books with social information, e.g. reviews, rates and tags, have emerged in large numbers on the web. However, there are several limitations in traditional search methods for social books, as social books include complicated and various social information. Relevance feedback is always an important and concerned technique in information retrieval. Therefore in this paper we propose a search system based on pseudo-relevance feedback (PRF) for expanding and enriching the social information of queries. In our system, First, Galago is used to get the initial rank list. Then relevance models are performed to select candidate high-frequent words that can be benefit to queries. Next, the original queries and these selected words are combined into new queries by linear smoothing. With evaluation on the INEX2012 / 2013 Social Book Search Track database, our proposed system has an encouraged performance (nDCG@10) compared to several state-of-the-art (contest) systems.