Comparing Simple Association-Rules and Repeat-Buying Based Recommender Systems in a B2B Environment

Geyer-Schulz, Andreas; Hahsler, Michael; Thede, Anke

doi:10.1007/978-3-642-18991-3_48

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Such a framework would also help new researchers in the field access a number of baselines they could compare their own approaches with. A framework could either be built from scratch, or be based on existing frameworks such as MyMediaLite, 33 LensKit, 34 Mahout, 35 Duine, 36 RecLab Core, 37 easyrec, 38 or Recommender101. 39 Finally, the community could benefit from considering research results from related disciplines.…”

Section: Discussionmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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Section: Discussionmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…Recommender systems are complex applications that are based on a combination of several models, algorithms and heuristics. This complexity makes evaluation efforts very difficult and results are hardly generalizable, which is apparent in the literature about recommender evaluation (Schulz & Hahsler, 2002). Previous research work on recommender system evaluation has mainly focused on algorithm accuracy, especially objective prediction accuracy.…”

Section: Preliminary Experimental Resultsmentioning

confidence: 99%

A Novel Strategy for Recommending Multimedia Objects and its Application in the Cultural Heritage Domain

Albanese

d’Acierno

Moscato

et al. 2011

International Journal of Multimedia Data Engineering and Management

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One of the most important challenges in the information access field, especially for multimedia repositories, is information overload. To cope with this problem, in this paper, the authors present a strategy for a recommender system that computes customized recommendations for users’ accessing multimedia collections, using semantic contents and low-level features of multimedia objects, past behaviour of individual users, and social behaviour of the users’ community as a whole. The authors implement their strategy in a recommender prototype for browsing image digital libraries in the Cultural Heritage domain. They then investigate the effectiveness of the proposed approach, based on the users’ satisfaction. The preliminary experimental results show that the approach is promising and encourages further research in this direction.

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“…Tag classification schemes using HGA‐SVM as classifier are used to generate graphical output for performance evaluation. The generated precision, recall, and F ‐measure show the performance of the classifier and classification system . Using weight scheme TF‐IDF for tag classification improves accuracy.…”

Section: Proposed Tag Classification Systemmentioning

confidence: 99%

Data mining‐based tag recommendation system: an overview

Subramaniyaswamy

Varadharajan

Indragandhi

et al. 2015

WIREs Data Min & Knowl

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The advent of high‐speed Internet connections has revolutionized the way research is being carried out to obtain relevant information. Conversely, retrieving pertinent information from the copious resources available is not only difficult but also time consuming. In the recent years, tagging activity has been perceived as a potential source of knowledge on personal preferences, interests, targets, goals, and other attributes. Tags allow users to effectively annotate resources using keywords to personalize their recommendations and organize the resources for easy retrieval. However, the preference of users varies extremely resulting in tagging being counterproductive. These shortcomings reduce the application of the tagging system for filtering as well as retrieval of information. The tag recommendation system becomes useful by suggesting a set of relevant keywords to annotate the resources. This paper presents a review of the tag recommendation systems and the constraints that affects the available tag recommendation systems. Furthermore, we propose the use of spreading activation algorithm to study the role of constructed topic ontology for efficient tag recommendations. This approach is founded on the assumption that tags that are recommended to the user are predicted from the extracted keywords from the existing blogs and the topics in constructed topic ontology. We have also proposed a tag classification system, namely Correlation‐based Feature Selection–Hybrid Genetic Algorithm and classifier HGA‐SVM (support vector machine), and have compared the results with results produced by other existing feature selection methods. The results obtained from the experiments have been presented. WIREs Data Mining Knowl Discov 2015, 5:87–112. doi: 10.1002/widm.1149 This article is categorized under: Algorithmic Development > Web Mining Technologies > Classification Technologies > Computational Intelligence

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Comparing Simple Association-Rules and Repeat-Buying Based Recommender Systems in a B2B Environment

Cited by 5 publications

References 7 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

A Novel Strategy for Recommending Multimedia Objects and its Application in the Cultural Heritage Domain

Data mining‐based tag recommendation system: an overview

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