DF-Miner: Domain-specific facet mining by leveraging the hyperlink structure of Wikipedia

Wei, Bifan; Li, Jun; Zheng, Qinghua; Zhang, Wei; Wang, Chenchen; Wu, Bei

doi:10.1016/j.knosys.2015.01.001

Cited by 13 publications

(3 citation statements)

References 31 publications

(44 reference statements)

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“…Sometimes, also the preference strength, e.g., must or wish, can be relevant [117]. While different approaches for mining possible facets from structured and unstructured text documents were proposed in the literature [149], the set of facets is often manually engineered based on domain expertise. Furthermore, in case the facets refer to functional requirements as in [55,152], additional knowledge has to be encoded in the system to match these requirements with the recommendable items.…”

Section: User Modelingmentioning

confidence: 99%

A Survey on Conversational Recommender Systems

Jannach,

Manzoor,

Cai

et al. 2020

Preprint

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Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future. CCS Concepts: • Information systems → Recommender systems; • General and reference → Surveys and overviews; • Human-centered computing → Interactive systems and tools;

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Section: User Modelingmentioning

confidence: 99%

A Survey on Conversational Recommender Systems

Jannach,

Manzoor,

Cai

et al. 2020

Preprint

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“…Wei and Liu [6,7], developed a novel system that they called DFT-Extractor and DF-Miner respectively which automati cally construct domain-specific faceted taxonomies from Wikipe dia in three steps. It first crawls the term of domains from Wipe dia.…”

Section: Related Workmentioning

confidence: 99%

“…In DF-Miner system [7], they proposed a new topology Approach to automatically acquire domain-specific fa cets. For this, they worked on Wikipedia article pages and cate gory pages as well as focused on automatic DF-mining based topological properties of hyperlink structure between Wikipedia pages.…”

Section: Related Workmentioning

confidence: 99%

Facets extraction-based approach for query recommendation using data mining approach

Zadgaonkar¹,

Agrawal²,

Aote³

2018

IJET

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Search engines are popularly utilized for extracting desired information from World Wide Web by users. Efficiency of these search engines are dependent on how fast search results can be retrieved and whether these results reflects the desired info or not. For a particular query, vast amount of relevant information is scattered across the multiple web pages. Search engines generate multiple web links as a output. It has been a jigsaw puzzle for users to identify and select relevant links to extract further desired information. To address this issue, we are proposing an approach for Query Recommendation for getting relevant search results from web using facet mining techniques. Facets are the semantically related words for a query which defines its multiple aspects. We are extracting these aspects of a query from Wikipedia pages which is considered to be a trustworthy resource on the web. Our proposed system uses various text processing techniques to refine the results using lexical resource like WorldNet. In this paper we are discussing our approach and its implementation and results obtained. In the paper , Discussion on future research direction is included to conclude.

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