Efficient diversity-aware search

Angel, Albert; Koudas, Nick

doi:10.1145/1989323.1989405

Cited by 102 publications

(95 citation statements)

References 30 publications

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“…redundant), and these users are chosen to provide recommendations to answer the query, the probability of retrieving the same set of relevant items increases and recall results remain low. In Information Retrieval, usefulness is used as a way to overcome redundancy between the items of a result list by combining relevance with diversity [8,9]. In our context, we claim that usefulness can be used when clustering user profiles in U-Net, instead of just relevance.…”

Section: Introductionmentioning

confidence: 93%

“…In the following, we show that usefulness(v j |v j+1 , ..., v n ) can be expressed into a known probabilistic diversification model [8,9]. In Formula 5 we first integrate usefulness (the right hand side of Formula 4) into a conditional probability.…”

Section: Usefulness Scorementioning

confidence: 99%

“…Similar to [8,9,13], we assume that the redundancy of a user profile v 1 with another user profile v 2 is independent of its redundancy with other users and we derive Formula 6.…”

Section: Usefulness Scorementioning

confidence: 99%

“…We show that usefulness is a good way to increase recall and that it should be expressed as a known probabilistic diversification score [8,9]. 2.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploiting Diversification in Gossip-Based Recommendation

Servajean

Pacitti

Liroz-Gistau

et al. 2014

Lecture Notes in Computer Science

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Abstract. In the context of Web 2.0, the users become massive producers of diverse data that can be stored in a large variety of systems. The fact that the users' data spaces are distributed in many different systems makes data sharing difficult. In this context of large scale distribution of users and data, a general solution to data sharing is offered by distributed search and recommendation. In particular, gossip-based approaches provide scalability, dynamicity, autonomy and decentralized control. Generally, in gossip-based search and recommendation, each user constructs a cluster of "relevant" users that will be employed in the processing of queries. However, considering only relevance introduces a significant amount of redundancy among users. As a result, when a query is submitted, as the user profiles in each user's cluster are quite similar, the probability of retrieving the same set of relevant items increases, and recall results are limited. In this paper, we propose a gossip-based search and recommendation approach that is based on a new clustering score, called usefulness, that combines relevance and diversity, and we present the corresponding new gossip-based clustering algorithm. We validate our proposal with an experimental evaluation using three datasets based on MovieLens, Flickr and LastFM. Compared with state of the art solutions, we obtain major gains with a three order of magnitude recall improvement when using the notion of usefulness regardless of the relevance score between two users used.

show abstract

Section: Introductionmentioning

confidence: 93%

Section: Usefulness Scorementioning

confidence: 99%

“…Similar to [8,9,13], we assume that the redundancy of a user profile v 1 with another user profile v 2 is independent of its redundancy with other users and we derive Formula 6.…”

Section: Usefulness Scorementioning

confidence: 99%

“…We show that usefulness is a good way to increase recall and that it should be expressed as a known probabilistic diversification score [8,9]. 2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting Diversification in Gossip-Based Recommendation

Servajean

Pacitti

Liroz-Gistau

et al. 2014

Lecture Notes in Computer Science

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show abstract

“…On the other hand, Gollapudi et al show that it is impossible to satisfy many diversity requirements simultaneously [15]. Angel et al [2] and Fraternali et al [13] study data access methods to efficiently find the top diversified results. Borodin et al [3] consider the max-sum diversification under matroid constraint.…”

Section: Related Workmentioning

confidence: 99%

Subgraph Search in Large Graphs with Result Diversification

Yu¹,

Yuan

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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The problem of subgraph search in large graphs has wide applications in both nature and social science. The subgraph search results are typically ordered based on graph similarity score. In this paper, we study the problem of ranking the subgraph search results based on diversification. We design two ranking measures based on both similarity and diversity, and formalize the problem as an optimization problem. We give two efficient algorithms, the greedy selection and the swapping selection with provable performance guarantee. We also propose a novel local search heuristic with at least 100 times speedup and a similar solution quality. We demonstrate the efficiency and effectiveness of our approaches via extensive experiments.

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Relevant Filtering in a Distributed Content‐based Publish/Subscribe System

Mouza¹,

Travers²

2018

NoSQL Data Models

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Efficient diversity-aware search

Cited by 102 publications

References 30 publications

Exploiting Diversification in Gossip-Based Recommendation

Exploiting Diversification in Gossip-Based Recommendation

Subgraph Search in Large Graphs with Result Diversification

Relevant Filtering in a Distributed Content‐based Publish/Subscribe System

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