A Language Modeling Approach to Personalized Search Based on Users’ Microblog Behavior

Younus, Arjumand; O’Riordan, Colm; Pasi, Gabriella

doi:10.1007/978-3-319-06028-6_83

Cited by 15 publications

(10 citation statements)

References 9 publications

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“…Much research has been conducted on personalization in IR. Different personalization approaches have been proposed based on different information sources about users such as user-generated content (e.g., See [2], [3]), social annotations (e.g., See [4]- [7]), social networks (e.g., See [8]- [10]), and history of users' interactions (e.g., See [11]). Since the focus of this paper is on using social-networks as information sources, we focus on social-network-based PIR methods in the following and mention a number of them.…”

Section: Related Workmentioning

confidence: 99%

“…In this explanation, the searching behavior of an individual looking for some information is assumed to be biased toward the communities he belongs to in the hierarchy of the communities. 2 The lower the community is in the hierarchy, the higher bias is assumed. For example, in a society with the network structure of Fig.…”

Section: Intuitionsmentioning

confidence: 99%

“…It is noteworthy that although many PIR methods are proposed in previous work, not all of them can be used as baselines for our comparisons since they are diverse in the resources they use for personalization. For example, some methods use social annotations (e.g., See [38]) and some use user-generated content (e.g., See [2]) as sources of information about users. Comparing methods relying on different information sources may not be fair.…”

Section: B Baselinesmentioning

confidence: 99%

“…imposed much overhead to HI and PHI (For example, about PHI, only inside the undivided cluster with 8 989 vertices, we had to compute 8 989 2 = 80 802 121 PPR values). After pre-computing the PPRs, we perform a retrieval for 500 natural queries with each of the methods.…”

Section: G Running Time Analysismentioning

confidence: 99%

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Search Personalization Based on Social-Network-Based Interestedness Measures

et al. 2019

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Personalized search methods try to tailor the search results to the user's needs and preferences. A popular source of information for personalization is social networks of users. In this paper, we aim at proposing a social-network-based personalized information retrieval (PIR) method that: i) is holistic (not just considering some local neighborhood of the searcher); ii) is efficient in term of computational/storage cost; iii) its personalization component has a probabilistic basis. To the best of our knowledge, no such method exists. We propose an interestedness measure and several variations of it for quantifying the interestedness of each user in another user based on a social network of users. The general idea is to try to mimic a searcher's behavior in the real world to estimate the interestedness. The measures are then exploited to personalize the retrieval results. We evaluate the resulting PIR methods and compare them in terms of retrieval effectiveness and computational/storage cost. We also compare them with some baseline methods. In summary, our analyses suggest that retrieval based on at least one of the measures performs well in terms of both retrieval effectiveness and computational/storage cost.INDEX TERMS Information retrieval, personalization, personalized pagerank, social networks.

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Section: Related Workmentioning

confidence: 99%

Section: Intuitionsmentioning

confidence: 99%

Section: B Baselinesmentioning

confidence: 99%

Section: G Running Time Analysismentioning

confidence: 99%

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Search Personalization Based on Social-Network-Based Interestedness Measures

et al. 2019

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“…This score is proportional to the preference degree of user to each concept. A personalized information retrieval based on social network is proposed in [7]. A new measure for measuring similarity between users in social network called interestedness measure was proposed in this work.…”

Section: Related Workmentioning

confidence: 99%

Personalization in Collaborative Fusion based Enterprise Information retrieval

L*,

2019

IJRTE

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Due to data spread across various heterogeneous data stores, information retrieval in Enterprise data stores is always challenging compared to web based retrieval systems. We have proposed a collaborative fusion based information retrieval in [1] using the observation on similar users tends to prefer similar search results. The solution applied three dimensions of user similarity, document similarity and user to documents affinity to a collaborative information fusion based retrieval. The work also proposed active feedback based search result revision to get highly relevant results. But the work did not have any provision for personalization and could not handle the cold start problems. Without consideration for cold start problems, the user to document affinity cannot be modeled accurately as the result, the collaborative fusion process is affected. In this work, we improve our earlier solution of collaborative fusion based information retrieval with consideration for user personalization and solution for cold start problems. The solution is based on query refinement using the information hidden in enterprise messaging systems. A user profile is built as vector of concepts using the information in enterprise messaging systems and this user profile concept vector is used to refine the query in way to personalize the results and avoid cold start problems. Compared to approach in [1], the proposed query refinement based personalization is able to increase the relevancy accuracy by 10% as obtained from experimental results.

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Exploring Reviews and Ratings on Reviews for Personalized Search

Yang

Cai

et al. 2016

Current Developments in Web Based Learning

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A Language Modeling Approach to Personalized Search Based on Users’ Microblog Behavior

Cited by 15 publications

References 9 publications

Search Personalization Based on Social-Network-Based Interestedness Measures

Search Personalization Based on Social-Network-Based Interestedness Measures

Personalization in Collaborative Fusion based Enterprise Information retrieval

Exploring Reviews and Ratings on Reviews for Personalized Search

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