Diversity-Conscious Retrieval

McSherry, David

doi:10.1007/3-540-46119-1_17

Cited by 104 publications

(85 citation statements)

References 6 publications

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“…From a user point of view, it is very desirable to have a suitable criterion to select a small interesting subset of graphs of the skyline GSS. One solution is to use the criterion of diversity [26] to select a subset of graphs which is as diverse as possible and then provide the user with a picture of the whole set GSS.…”

Section: Refining Graph Similarity Skylinementioning

confidence: 99%

A similarity skyline approach for handling graph queries - A preliminary report

Abbaci

Hadjali

Liétard

et al. 2011

2011 IEEE 27th International Conference on Data Engineering Workshops

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Abstract-One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an alternative approach allowing for searching similar graphs to a graph query where similarity between graphs is rather modeled by a vector of scalars than a unique scalar. To this end, we introduce the notion of similarity skyline of a graph query defined by the subset of graphs of the target database that are the most similar to the query in a Pareto sense. The idea is to achieve a d-dimensional comparison between graphs in terms of local distance (or similarity) measures and to retrieve those graphs that are maximally similar in the sense of the Pareto dominance relation. A diversity-based method for refining the retrieval result is proposed as well.

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Section: Refining Graph Similarity Skylinementioning

confidence: 99%

A similarity skyline approach for handling graph queries - A preliminary report

Abbaci

Hadjali

Liétard

et al. 2011

2011 IEEE 27th International Conference on Data Engineering Workshops

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“…As we shall see below, recommenders operating at reduced numeric precision do not suffer greatly in accuracy and so the cascade hybrid is a reasonable option. McSherry [29] uses a similar idea in creating regions of similarity in which scores vary no more than a given ε to satisfy the goal of increasing recommendation diversity.…”

Section: Cascadementioning

confidence: 99%

Hybrid Web Recommender Systems

Burke

Lecture Notes in Computer Science

912

525

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Abstract. Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths. IntroductionRecommender systems are personalized information agents that provide recommendations: suggestions for items likely to be of use to a user [18,41,42]. In an ecommerce context, these might be items to purchase; in a digital library context, they might be texts or other media relevant to the user's interests. 1 A recommender system can be distinguished from an information retrieval system by the semantics of its user interaction. A result from a recommender system is understood as a recommendation, an option worthy of consideration; a result from an information retrieval system is interpreted as a match to the user's query. Recommender systems are also distinguished in terms of personalization and agency. A recommender system customizes its responses to a particular user. Rather than simply responding to queries, a recommender system is intended to serve as an information agent. 2 1 In this chapter, I use the e-commerce term "products" to refer to the items being recommended, with the understanding that other information-seeking contexts are also pertinent. 2 Techniques such as relevance feedback enable an information retrieval engine to refine its representation of the user's query, and therefore can be seen as a simple form of recommendation. The search engine Google (http://www.google.com) blurs this distinction further, using "authoritativeness" criteria in addition to strict matching [6]. R. BurkeA variety of techniques have been proposed as the basis for recommender systems: collaborative, content-based, knowledge-based, and demographic techniques are surveyed below. Each of these techniques has known shortcomings, such as the wellknown cold-start problem for collaborative and content-based systems (what to do with new users with few ratings) and the knowledge engineering bottleneck in knowledge-based approaches. A hybrid recommender system is one that combines multiple techniques together to achieve some synergy between them. For example, a collaborative system and a knowledge-based system might be combined so that the knowledgebased component can compensate for the cold-start problem, providing recommendations to new users whose profiles are too small to give the collaborative technique any traction, and the collaborative component can work it...

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“…11.10. The approach described in [55,56] partitions the case base into similarity layersgroups of cases with equivalent similarity to the target query-and the retrieved cases are chosen starting with the highest similarity layer and until k cases have been selected. The final cases selected from the lowest necessary similarity layer are chosen based on an optimal diversity maximizing technique.…”

Section: Retrieval Setmentioning

confidence: 99%

“…For example, [55] shows that it is sometimes possible to enhance diversity without loss of query similarity and a related approach based on the idea of similarity layers is described [56]. Very briefly, a set of cases, ranked by their similarity to the target query are partitioned into similarity layers, such that all cases in a given layer have the same similarity value to the query.…”

Section: Retrieval Setmentioning

confidence: 99%

“…However, the diversity improvements obtained are typically less than those achieved by the bounded greedy algorithm, because all cases from higher similarity layers are always included without any diversity enhancement. An alternative, and more flexible, diversity enhancing approach is also introduced based on the analogous notion of similarity intervals; see also [56]. The advantage of this approach is that it can achieve greater diversity improvements by relaxing the constraint that query similarity must be preserved.…”

Section: Retrieval Setmentioning

confidence: 99%

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Case-Based Recommendation

Smyth

Lecture Notes in Computer Science

154

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Abstract.Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems. IntroductionRecently I wanted to buy a new digital camera. I had a vague idea of what I wanted-a 6 mega-pixel digital SLR from a good manufacturer-but it proved difficult and time consuming to locate a product online that suited my needs, especially as these needs evolved during my investigations. Many online stores allowed me to browse or navigate through their product catalog by choosing from a series of static features (e.g., manufacturer, camera type, resolution, level of zoom etc.). Each time I selected a feature I was presented with the set of cameras with this feature and I could then go on to choose another feature to further refine the presented products. Other stores allowed me to search for my ideal camera by entering a query (e.g. "digital slr, 6 mega-pixels") and presented me with a list of results which I could then browse at my leisure.Both of these access options were helpful in different ways-in the beginning I preferred to browse through catalogs but, after getting a feel for the various features and compromises, I tended to use search-based interfaces-however neither provided P. Brusilovsky,

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Diversity-Conscious Retrieval

Cited by 104 publications

References 6 publications

A similarity skyline approach for handling graph queries - A preliminary report

A similarity skyline approach for handling graph queries - A preliminary report

Hybrid Web Recommender Systems

Case-Based Recommendation

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