Data Mining Practical Machine Learning Tools and Techniques

The Long Tail: How Technology is turning mass markets into millions of niches. (p. 15). This passage from The Long Tail, pretty much sums it all up. The Long Tail by Chris Anderson is a good and worthwhile read for information scientists, computer scientists, ecommerce researchers, and others interested in all areas of Web research. The central premise of the book is that the combination of (1) the Pareto or Zipf distribution (i.e., power law probability distribution) that is characteristic of Web traffic and (2) the direct access to consumers via Web technology has opened up new business opportunities in the ''long tail''. Producers and advertisers no longer have to target ''the big hits'' at the head of the distribution. Instead, they can target the small, niche communities or even individuals in the tail of the distribution. The long tail is has been studied by Web researchers and has been noted in term usage on search engines, access times to servers, and popularity of Web sites. Andersen points out that the long tail also applies to products sold on the Web. He recounts that a sizeable percentage of Amazon sales come from books that only sell a few copies, a large number of songs from Rhapsody get downloaded only once in a month, and a significant number of movies from Netflix only get ordered occasionally. However, since the storage is in digital form for the songs and music (and Amazon out sources the storage of books) there is little additional inventory cost of these items. This phenomenon across all Web companies has led to a broadening of participation by both producers and consumers that would not have happened without the Web. The idea of the long tail is well known, of course. What Anderson has done is present it in an interesting manner and in a Web ecommerce setting. He applies it to Web businesses and then relates the multitude of other factors ongoing that permit the actual implementation of the long tail effect. Anderson also expands on prior work on the long tail by introducing an element of time, given the distribution a three dimensional effect. All in all, it is a nifty idea. The book is comprised of 14 chapters, plus an Introduction. Chapter 1 presents an overview of what the long tail is. Chapter 2 discusses the ''head'', which is the top of the tail where the …

The Long Tail: Why the Future of Business is Selling Less of More

Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user. In this introductory chapter, we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook. Additionally, we aim to help the reader navigate the rich and detailed content that this handbook offers.

Recommender Systems: Introduction and Challenges

Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel context-aware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music.

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Matrix factorization techniques for context aware recommendation

As computer science and information technology are making broad and deep impacts on our daily lives, more and more papers are being submitted to computer science journals and conferences. To help authors decide where they should submit their manuscripts, we present the Content-based Journals & Conferences Recommender System on computer science, as well as its web service at http://www.keaml.cn/prs/ . This system recommends suitable journals or conferences with a priority order based on the abstract of a manuscript. To follow the fast development of computer science and technology, a web crawler is employed to continuously update the training set and the learning model. To achieve interactive online response, we propose an efficient hybrid model based on chi-square feature selection and softmax regression. Our test results show that, the system can achieve an accuracy of 61.37% and suggest the best journals or conferences in about 5  s on average.

A content-based recommender system for computer science publications

What makes a good recommendation or good list of recommendations?Research into recommender systems has traditionally focused on accuracy, in particular how closely the recommender’s predicted ratings are to the users’ true ratings. However, it has been recognized that other recommendation qualities—such as whether the list of recommendations is diverse and whether it contains novel items—may have a significant impact on the overall quality of a recommender system. Consequently, in recent years, the focus of recommender systems research has shifted to include a wider range of “beyond accuracy” objectives.In this article, we present a survey of the most discussed beyond-accuracy objectives in recommender systems research: diversity, serendipity, novelty, and coverage. We review the definitions of these objectives and corresponding metrics found in the literature. We also review works that propose optimization strategies for these beyond-accuracy objectives. Since the majority of works focus on one specific objective, we find that it is not clear how the different objectives relate to each other.Hence, we conduct a set of offline experiments aimed at comparing the performance of different optimization approaches with a view to seeing how they affect objectives other than the ones they are optimizing. We use a set of state-of-the-art recommendation algorithms optimized for recall along with a number of reranking strategies for optimizing the diversity, novelty, and serendipity of the generated recommendations. For each reranking strategy, we measure the effects on the other beyond-accuracy objectives and demonstrate important insights into the correlations between the discussed objectives. For instance, we find that rating-based diversity is positively correlated with novelty, and we demonstrate the positive influence of novelty on recommendation coverage.

Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems

Context aware recommender systems (CARS) adapt to the specific situation in which the recommended item will be consumed. So, for instance, music recommendations while the user is traveling by car should take into account the current traffic condition or the driver’s mood. This requires the acquisition of ratings for items in several alternative contextual situations, to extract from this data the true dependency of the ratings on the contextual situation. In this paper, in order to simplify the in-context rating acquisition process, we consider the individual perceptions of the users about the influence of context on their decisions. We have elaborated a system design methodology where we assume that users can be requested to judge: a) if a contextual factor (e.g., the traffic state) is relevant for their decision making task, and b) how they would rate an item assuming that a certain contextual condition (e.g., traffic is chaotic) holds. Using these evaluations we show that it is possible to build an effective context-aware mobile recommender system.

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InCarMusic: Context-Aware Music Recommendations in a Car

https://www.inf.unibz.it/~ricci/papers/lit_review_music-context.pdf

Contextual music information retrieval and recommendation: State of the art and challenges

This chapter gives an introduction to music recommender systems research. We highlight the distinctive characteristics of music, as compared to other kinds of media. We then provide a literature survey of content-based music recommendation, contextual music recommendation, hybrid methods, and sequential music recommendation, followed by overview of evaluation strategies and commonly used data sets. We conclude by pointing to the most important challenges faced by music recommendation research.

Music Recommender Systems

In this paper, we present an ongoing research work on the design and development of a generic knowledge-based description framework built upon semantic networks. It aims at integrating and exploiting knowledge on several domains to provide cross-domain item recommendations. More specifically, we propose an approach that automatically extracts information about two different domains, such as architecture and music, which are available in Linked Data repositories. This enables to link concepts in the two domains by means of a weighted directed acyclic graph, and to perform weight spreading on such graph to identify items in the target domain (music artists) that are related to items of the source domain (places of interest).

/pdf/a-generic-semantic-based-framework-for-cross-domain-168fxds14h.pdf

Marius Kaminskas

Papers

Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems

InCarMusic: Context-Aware Music Recommendations in a Car

Contextual music information retrieval and recommendation: State of the art and challenges

Music Recommender Systems

A generic semantic-based framework for cross-domain recommendation