Abstract
Temporal information retrieval has been a topic of great interest in recent years. Despite the efforts that have been conducted so far, most popular search engines remain underdeveloped when it comes to explicitly considering the use of temporal information in their search process. In this paper we present GTE-Rank, an online searching tool that takes time into account when ranking time-sensitive query web search results. GTE-Rank is defined as a linear combination of topical and temporal scores to reflect the relevance of any web page both in topical and temporal dimensions. The resulting system can be explored graphically through a search interface made available for research purposes.

Abstract
With the emergence of online Music Streaming Services (MSS) such as Pandora and Spotify, listening to music online became very popular. Despite the availability of these services, users face the problem of finding among millions of music tracks the ones that match their music taste. MSS platforms generate interaction data such as users' defined playlists enriched with relevant metadata. These metadata can be used to predict users' preferences and facilitate personalized music recommendation. In this work, we aim to infer music tastes of users by using personal playlist information. Characterizing users' taste is important to generate trustable recommendations when the amount of usage data is limited. Here, we propose to predict the users' preferred music feature's value (e.g. Genre as a feature has different values like P op, Rock, etc.) by modeling, not only usage information, but also music description features. Music attribute information and usage data are typically dealt with separately. Our method FPMF (Feature Prediction based on Matrix Factorization) treats music feature values as virtual users and retrieves the preferred feature values for real target users. Experimental results indicate that our proposal is able to handle the item cold start problem and can retrieve preferred music feature values with limited usage data. Furthermore, our proposal can be useful in recommendation explanation scenarios. © 2016 ACM.

Abstract
Temporal information retrieval has been a topic of great interest in recent years. Its purpose is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries. In this article, we present a survey of the existing literature on temporal information retrieval. In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches. We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area. Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches. Moreover, an effective end-to-end temporal retrieval system that exploits temporal information to improve the quality of the presented results remains undeveloped.

Abstract
Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information sy Analysis stem that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the Web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites. © 2015, IGI Global.

Abstract

Abstract
In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as "Philip Seymour Hoffman" where the results may require no recency at all. In this work, we focus on this type of queries named "time-sensitive queries" where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community.