Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top

2015 | Book

Using Implicit Preference Relations to Improve Content Based Recommending Read chapter Read first chapter

E-Commerce and Web Technologies

16th International Conference on Electronic Commerce and Web Technologies, EC-Web 2015, Valencia, Spain, September 2015, Revised Selected Papers

Editors: Heiner Stuckenschmidt, Dietmar Jannach

Publisher: Springer International Publishing

Book Series : Lecture Notes in Business Information Processing

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Login to get access
insite
SEARCH

About this book

This book constitutes the revised proceedings of the 16th International Conference on Electronic Commerce and Web Technologies (EC-Web) held in Valencia, Spain, in September 2015.

The 10 full papers included in this volume were carefully reviewed and selected from 28 submissions. The papers are organized in topical sections on recommender systems, multimedia recommendation, social and semantic web; and process management.

Table of Contents

Frontmatter

Recommender Systems

Frontmatter
Using Implicit Preference Relations to Improve Content Based Recommending
Abstract
Our work is generally focused on recommending for small or medium-sized e-commerce portals, where we are facing scarcity of explicit feedback, low user loyalty, short visit times or low number of visited objects. In this paper, we present a novel approach to use specific user behavior as implicit feedback, forming binary relations between objects. Our hypothesis is that if user select some object from the list of displayed objects, it is an expression of his/her binary preference between selected and other shown objects. These relations are expanded based on content-based similarity of objects forming partial ordering of objects. Using these relations, it is possible to alter any list of recommended objects or create one from scratch.
We have conducted several off-line experiments with real user data from a Czech e-commerce site with keyword based VSM and SimCat recommenders. Experiments confirmed competitiveness of our method, however on-line A/B testing should be conducted in the future work.
Ladislav Peska, Peter Vojtas
Product Recommendation for Small-Scale Retailers
Abstract
Product recommendation in e-commerce is a widely applied technique which has been shown to bring benefits in both product sales and customer satisfaction. In this work we address a particular product recommendation setting — small-scale retail websites where the small amount of returning customers makes traditional user-centric personalization techniques inapplicable. We apply an item-centric product recommendation strategy which combines two well-known methods – association rules and text-based similarity – and demonstrate the effectiveness of the approach through two evaluation studies with real customer data.
Marius Kaminskas, Derek Bridge, Franclin Foping, Donogh Roche
Using Graph Metrics for Linked Open Data Enabled Recommender Systems
Abstract
Linked Open Data has been recognized as a useful source of background knowledge for building content-based recommender systems. While many existing approaches transform that data into a propositional form, we investigate how the graph nature of Linked Open Data can be exploited when building recommender systems. In particular, we use path lengths, the K-Step Markov approach, as well as weighted NI paths to compute item relevance and perform a content-based recommendation. An evaluation on the three tasks of the 2015 LOD-RecSys challenge shows that the results are promising, and, for cross-domain recommendations, outperform collaborative filtering.
Petar Ristoski, Michael Schuhmacher, Heiko Paulheim

Multimedia Recommendation

Frontmatter
Toward Building a Content-Based Video Recommendation System Based on Low-Level Features
Abstract
One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular movie-sharing websites, such as Youtube, everyday, hundred millions of hours of videos are uploaded and big portion of these videos may not contain any meta-data, to be used by the system to generate recommendations.
In this paper, we address this problem by proposing a method, that is based on automatic analysis of the video content in order to extract a number representative low-level visual features. Such features are then used to generate personalized content-based recommendations. Our evaluation shows that our proposed method can outperform the baselines, by producing more relevant recommendations. Hence, a set low-level features extracted automatically can be more descriptive and informative of the video content than a set of high-level expert annotated features.
Yashar Deldjoo, Mehdi Elahi, Massimo Quadrana, Paolo Cremonesi
Personalized and Context-Aware TV Program Recommendations Based on Implicit Feedback
Abstract
The current explosion of the number of available channels is making the choice of the program to watch an experience more and more difficult for TV viewers. Such a huge amount obliges the users to spend a lot of time in consulting TV guides and reading synopsis, with a heavy risk of even missing what really would have interested them. In this paper we confront this problem by developing a recommender system for TV programs. Recommender systems have been widely studied in the video-on-demand field, but the TV domain poses its own challenges which make the traditional video-on-demand techniques not suitable. In more detail, we propose recommendation algorithms relying exclusively on implicit feedback and leveraging context information. An extensive evaluation on a real TV dataset proves the effectiveness of our approach, and in particular the importance of the context in providing TV program recommendations.
Paolo Cremonesi, Primo Modica, Roberto Pagano, Emanuele Rabosio, Letizia Tanca
An LDA Topic Model Adaptation for Context-Based Image Retrieval
Abstract
In the context-based image retrieval, the textual information surrounding the image plays a central role for ranking returned results. Although this technique outperforms content-based approaches, it may fail when the query keywords does not match the textual content of many documents containing relevant images. In addition, users are usually not experts and provide ambiguous queries that lead to heterogeneous results. To solve these problems, researchers are trying to re-rank primary results using other techniques such as query expansion, concept-based retrieval, etc. In this paper, we propose to use LDA topic model to re-rank results and therefore improve retrieval precision. We apply this model in two levels: global level represented by the whole document containing the image and local level represented by the paragraph containing an image (considered as a specific textual information for the image). Results show a significant improvement over the standard text retrieval approach by re-ranking with the LDA model applied to the local level.
Hatem Aouadi, Mouna Torjmen Khemakhem, Maher Ben Jemaa

Social and Semantic Web

Frontmatter
Exploiting Microdata Annotations to Consistently Categorize Product Offers at Web Scale
Abstract
Semantically annotated data, using markup languages like RDFa and Microdata, has become more and more publicly available in the Web, especially in the area of e-commerce. Thus, a large amount of structured product descriptions are freely available and can be used for various applications, such as product search or recommendation. However, little efforts have been made to analyze the categories of the available product descriptions. Although some products have an explicit category assigned, the categorization schemes vary a lot, as the products originate from thousands of different sites. This heterogeneity makes the use of supervised methods, which have been proposed by most previous works, hard to apply. Therefore, in this paper, we explain how distantly supervised approaches can be used to exploit the heterogeneous category information in order to map the products to set of target categories from an existing product catalogue. Our results show that, even though this task is by far not trivial, we can reach almost \(56\,\%\) accuracy for classifying products into 37 categories.
Robert Meusel, Anna Primpeli, Christian Meilicke, Heiko Paulheim, Christian Bizer
The Interactive Effect of Review Rating and Text Sentiment on Review Helpfulness
Abstract
Review ratings and text sentiments respectively represent quantitative and qualitative aspects of user-generated product reviews. These two types of polarity information complement each other in affecting consumers’ review evaluation. Few extant studies consider the interplay of review rating and text sentiment on perceived review helpfulness. In this study, we attempt to investigate this potential interaction effect and examine whether it is conditional on review length. The empirical results from an analysis of 70,610 restaurant reviews from Yelp.com indicate that both review ratings and text sentiments exhibit negativity bias effect, such that negative ratings and texts are more helpful than positive ones. Meanwhile, the two types of review valence have a positive interaction effect on perceived review helpfulness. Moreover, the interaction effect of review rating and text sentiment is stronger for longer reviews.
Shasha Zhou, Bin Guo
A Twitter View of the Brazilian Stock Exchange Market
Abstract
In this paper, we present a view of the Brazilian stock exchange market based on a large characterization and analysis of Twitter data. In our analysis, we show that events and news about the stock market are capable of generating peaks of publications by Twitter users and that the frequency of posts follows the starting of the exchange trading day and maintains for about three hours after the stock market closing hour. Moreover, based on a survey conducted with a specific niche of Twitter users, we have been able to estimate that 0.5 % of those users have some knowledge of the Brazilian stock market and are mostly individual investors interested in publishing and consuming news about this market, having 45 % of them used Twitter as a source for investment decisions. Finally, we have observed that the total number of orders and the financial volume are positively correlated for 66 % of the stocks mentioned on Twitter, whereas the oscillation and maximum oscillation dimensions present no correlation.
Hugo S. Santos, Alberto H. F. Laender, Adriano C. M. Pereira

Process Management

Frontmatter
Towards Smart Logistics Process Management
Abstract
Logistics processes are generally agreed-upon, long running propositions between multiple partners, which are specified over Service Level Agreements as constraints to be maintained. However, these constraints can be violated at any time due to various unforeseen events that may stem from the process evolving context, leading the process to end up in unfortunate situations. In this paper, we present our framework that correlates critical business operations together with contextual events in order to predict possible violations prior to their occurrences while proactively generating mitigation countermeasures. In addition we develop a software and experiment it to demonstrate the practical applicability of the framework.
Raef Mousheimish, Yehia Taher, Béatrice Finance
Backmatter
Metadata
Title
E-Commerce and Web Technologies
Editors
Heiner Stuckenschmidt
Dietmar Jannach
Copyright Year
2015
Electronic ISBN
978-3-319-27729-5
Print ISBN
978-3-319-27728-8
DOI
https://doi.org/10.1007/978-3-319-27729-5

Premium Partner

    Image Credits