Genre Prediction to Inform the Recommendation Process

In this paper we present a time-based genre prediction strategy that can inform the book recommendation process. To explicitly consider time in predicting genres of interest, we rely on a popular time series forecasting model as well as reading patterns of each individual reader or group of readers (in case of libraries or publishing companies). Based on a conducted initial assessment using the Amazon dataset, we demonstrate our strategy outperforms its baseline counter-part

When the System Becomes Your Personal Docent: Curated Book Recommendations

Curation is the act of selecting, organizing, and presenting content most often guided by professional or expert knowledge. While many popular applications have attempted to emulate this process by turning users into curators, we put an accent on a recommendation system which can leverage multiple data sources to accomplish the curation task. We introduce QBook, a recommender that acts as a personal docent by identifying and suggesting books tailored to the various preferences of each individual user. The goal of the designed system is to address several limitations often associated with recommenders in order to provide diverse and personalized book recommendations that can foster trust, effectiveness of the system, and improve the decision making process. QBook considers multiple perspectives, from analyzing user reviews, user historical data, and items\u27 metadata, to considering experts\u27 reviews and constantly evolving users\u27 preferences, to enhance the recommendation process, as well as quality and usability of the suggestions. QBook pairs each generated suggestion with an explanation that (i) showcases why a particular book was recommended and (ii) helps users decide which items, among the ones recommended, will best suit their individual interests. Empirical studies conducted using the Amazon/LibraryThing benchmark corpus demonstrate the correctness of the proposed methodology and QBook\u27s ability to outperform baseline and state-of-the-art methodologies for book recommendations

From Recommendation to Curation: When the System Becomes Your Personal Docent

Curation is the act of selecting, organizing, and presenting content. Some applications emulate this process by turning users into curators, while others use recommenders to select items, seldom achieving the focus or selectivity of human curators. We bridge this gap with a recommendation strategy that more closely mimics the objectives of human curators. We consider multiple data sources to enhance the recommendation process, as well as the quality and diversity of the provided suggestions. Further, we pair each suggestion with an explanation that showcases why a book was recommended with the aim of easing the decision making process for the user. Empirical studies using Social Book Search data demonstrate the effectiveness of the proposed methodology

Discriminative Link Prediction using Local Links, Node Features and Community Structure

A link prediction (LP) algorithm is given a graph, and has to rank, for each node, other nodes that are candidates for new linkage. LP is strongly motivated by social search and recommendation applications. LP techniques often focus on global properties (graph conductance, hitting or commute times, Katz score) or local properties (Adamic-Adar and many variations, or node feature vectors), but rarely combine these signals. Furthermore, neither of these extremes exploit link densities at the intermediate level of communities. In this paper we describe a discriminative LP algorithm that exploits two new signals. First, a co-clustering algorithm provides community level link density estimates, which are used to qualify observed links with a surprise value. Second, links in the immediate neighborhood of the link to be predicted are not interpreted at face value, but through a local model of node feature similarities. These signals are combined into a discriminative link predictor. We evaluate the new predictor using five diverse data sets that are standard in the literature. We report on significant accuracy boosts compared to standard LP methods (including Adamic-Adar and random walk). Apart from the new predictor, another contribution is a rigorous protocol for benchmarking and reporting LP algorithms, which reveals the regions of strengths and weaknesses of all the predictors studied here, and establishes the new proposal as the most robust.Comment: 10 pages, 5 figure

Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page