One-and-only item recommendation with fuzzy logic techniques

Cornelis, Chris; Lu, Jie; Guo, Xuetao; Zhang, Guanquang

doi:10.1016/j.ins.2007.07.001

Cited by 83 publications

(51 citation statements)

References 26 publications

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“…For over 5 months we logged the explicit and implicit user feedback provided about the events by means of rating systems and page visits. This events dataset was particularly interesting to test our recommendation algorithm because events are one-and-only items [9] and difficult to recommend with a non-hybrid recommender.…”

Section: Resultsmentioning

confidence: 99%

A File-Based Approach for Recommender Systems in High-Performance Computing Environments

Dooms

Pessemier

Martens

2011

2011 22nd International Workshop on Database and Expert Systems Applications

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Abstract-Since recommendation systems tackle the problem of information overload, the processing of huge datasets can not be avoided. When these datasets no longer fit into the RAM memory of a computing node, a scalable data storage approach is required. While database systems are frequently used for this goal, they have their disadvantages and when not properly designed may slow down the recommendation process. In this paper we propose an alternative file-based data storage approach that is particularly well suited for a high-performance computing environment where the usage of databases may not always be an option.By breaking down the recommendation process in separate phases and carefully structuring the input and output of each phase, we have build a file-based recommendation system that scales proportional with the number of computing nodes and processor cores available in each node.

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Section: Resultsmentioning

confidence: 99%

A File-Based Approach for Recommender Systems in High-Performance Computing Environments

Dooms

Pessemier

Martens

2011

2011 22nd International Workshop on Database and Expert Systems Applications

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“…Each factor F is computed as a value ranging from 0 to 100, normalizing the result of the computation, if needed 4 . We also compute a qualitative version of the value, that is produced by using two thresholds 0 ≤ θ min < θ max ≤ 100.…”

Section: Rankingmentioning

confidence: 99%

“…For example, Facebook 1 "events" can be regarded as activities according to this definition. Activities, as "one-and-only items" [4] have some peculiarities:…”

Section: Introductionmentioning

confidence: 99%

Selection and Ranking of Activities in the Social Web

Lombardi

Likavec

Picardi

et al. 2013

AI*IA 2013: Advances in Artificial Intelligence

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Abstract. The paper presents a framework for the selection and ranking of activities proposed to users in a social networking service, as for example Facebook's "social events". Our proposal takes into account the peculiarities of this application area, considering a variety of factors including the spatiotemporal relations between activities and between activities and users, and the social opinions weighted by the user's interest in the specific themes and type of an activity. To this aim, we describe a semantic model for the involved entities, and especially for the available activities. We describe a process that can be easily configured according to user preferences, extended to take into account additional ranking factors, and adapted to include existing recommendation/ranking strategies. Finally, we specifically introduce ranking factors that can be used within the specific model and process we describe. The results of a preliminary evaluation show the effectiveness of our approach.

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“…This time dependency of one-and-only items, like events, has been tackled in related research. Cornelis et al developed a framework that uses fuzzy logic, allowing to reflect the uncertain information in the domain and considering the time factors of events [11]. However, the computational complexity of such a solution is high, making it difficult to employ on large-scale event distribution systems.…”

Section: Traditional Collaborative Filteringmentioning

confidence: 99%

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Pessemier

Coppens

Geebelen

et al. 2011

Multimed Tools Appl

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Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel.In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.

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One-and-only item recommendation with fuzzy logic techniques

Cited by 83 publications

References 26 publications

A File-Based Approach for Recommender Systems in High-Performance Computing Environments

A File-Based Approach for Recommender Systems in High-Performance Computing Environments

Selection and Ranking of Activities in the Social Web

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

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