Recommender Systems in E-Commerce

2006 ◽  
pp. 486-493
Author(s):  
A. B. Gil ◽  
F. J. Garcia
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
User Interfaces ◽  
Analogical Reasoning ◽  
Information Overload ◽  
Product Information ◽  
Specific Domain ◽  
Set Up ◽  
The Right ◽  
The Cost

Electronic commerce (EC) is, at first sight, an electronic means to exchange large amounts of product information between users and sites. This information must be clearly written since any users who accesses the site must understand it. Given the large amounts of information available at the site, interaction with an e-market site becomes an effort. It is also time-consuming, and the user feels disoriented as products and clients are always on the increase. One solution to make online shopping easier is to endow the EC site with a recommender system. Recommender systems are implanted in EC sites to suggest services and provide consumers with the information they need in order to decide about possible purchases. These tools act as a specialized salesperson for the customer, and they are usually enhanced with customization capabilities; thus they adapt themselves to the users, basing themselves on the analysis of their preferences and interests. Recommenders rely mainly on user interfaces, marketing techniques, and large amounts of information about other customers and products; all this is done, of course, in an effort to propose the right item to the right customer. Besides, recommenders are fundamental elements in sustaining usability and site confidence (Egger, 2001); that’s the reason why e-market sites give them an important role in their design (Spiekermann & Paraschiv, 2002). If a recommender system is to be perceived as useful by its users, it must address several problems, such as the lack of user knowledge in a specific domain, information overload, and a minimization of the cost of interaction. EC recommenders are gradually becoming powerful tools for EC business (Gil & García, 2003) making use of complex mechanisms mainly in order to support the user’s decision process by allowing the analogical reasoning by the human being, and avoiding the disorientation process that occurs when one has large amounts of information to analyse and compare. This article describes some fundamental aspects in building real recommenders for EC. We will first set up the scenario by exposing the importance of recommender systems in EC, as well as the stages involved in a recommender-assisted purchase. Next, we will describe the main issues along three main axes: first, how recommender systems require a careful elicitation of user requirements; after that, the development and tuning of the recommendation algorithms; and, finally, the design and usability testing of the user interfaces. Lastly, we will show some future trends in recommenders and a conclusion.

scholarly journals User-Query Processing through Dynamic Tweets Status Recommender System

2021 ◽  
Vol 58 (1) ◽  
pp. 5600-5606
Author(s):  
V. Kakulapati, D. Vasumathi, G. Suryanarayana
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
User Interfaces ◽  
Online Social Networks ◽  
Information Overload ◽  
Relevant Information ◽  
Twitter User ◽  
User Query ◽  
Text Filtering ◽  
Twitter Users

With increasing user information volume in online social networks, recommender systems have been an effective method to limit such information overload. The requirements of recommender systems specified, with widespread adoption in many internet social Twitter, Facebook, and Google online applications. In recent years,  the  micro-blogging  in  Twitter  has  brought  greater  importance  to  online  users  as  a  channel  spreading knowledge  and  information.  Through  Twitter,  users  can  find  the  relevant  information  on  the  search  they perform,  but  understanding  the  past,  present,  and  future  information  relevant  to  the  investigation  source  is needed real-time information. Estimating the successful tweet status (history, ongoing, and prospective) among the huge population of Twitter members is important to satisfy the needs of Twitter online content readers. In this paper, a Dynamic Tweets Status Recommender System (DTSRS) is designed by creating a set of dynamic recommendations to a Twitter user based on usability, consisting of people who post tweets, which is exciting present and future. The proposed recommender system is implemented through two approaches: the first is to analyze  the  Twitter  member  online  tweets,  select  and  understand  the  content  of  that  tweet,  and  the  second predicts  the  understanding  of  the  tweet  content,  suggest  the  dynamic  status  of  the  tweets.  In  this  paper,  the Twitter user tweets' views are expressed after examining the depth of content, different types of user interfaces, text filtering, and machine learning technique. The set of results through tweets experimentations with database operators carried out to evaluate and comparability the proposed recommender system's performance.  

Balqis restaurant: how to move on?

2017 ◽  
Vol 7 (3) ◽  
pp. 1-19
Author(s):  
Farzana Quoquab ◽  
Shazwani Binti Ahmad ◽  
Wan Nurul Syazwani Binti Wan Danial ◽  
Jihad Mohammad
Keyword(s):  
Subject Area ◽  
Marketing Mix ◽  
Business Success ◽  
Strategic Decisions ◽  
Gradual Improvement ◽  
Content Type ◽  
To Come ◽  
Set Up ◽  
The Right ◽  
The Cost

Subject area This case can be used in marketing management as well as consumer behaviour courses. Study level/applicability This case is suitable to use in advanced undergraduate levels, MBA and MSc in marketing courses that cover topics related to market segmentation and marketing mix strategies. Case overview This case highlights the dilemma of an entrepreneur and a manager of a restaurant who were to take a decision about the sustainability of their restaurant business. Balqis Restaurant was owned by Danny who was a retiree from Telekom Malaysia. He wanted to open a restaurant business after he came back from his long holiday trip. He conducted market research to find a suitable place to open his Arabic restaurant. He assigned Waleed Masood Abdullah as the manager of Balqis Restaurant. Finally, in June 2010, he opened his long awaited restaurant at Gombak, Kuala Lumpur. The restaurant was known as Qasar before the name was changed to Balqis in 2015 because of copyright issues related to Saba’ restaurant at Cyberjaya. The restaurant was well managed under Danny’s supervision for 4 years and successfully won customers’ hearts and loyalty before he decided to give full responsibility to Waleed in March 2014. Danny trusted Waleed because he taught and trained him. However, under Waleed’s management, Balqis started to lose its customers. Waleed also started to branch out the restaurant to different places in different states; one in Ipoh, and the other in Perak. He invested much money on renovation for all three branches, but one of the restaurants closed down in September 2014. This is because of the fact that they could no longer bear the cost of operations for the restaurant. However, he failed to learn from the mistake; they set up another restaurant, which was in Kuantan, in the same month. The sales were not that encouraging but it did show gradual improvement; yet, they once again sold it to another Arab businessman. Waleed realized his failure in managing the restaurant business in August 2015. He again opted to open another new branch which was questioned by Danny. He was in a rush to open it by the end of December 2015 to ensure that the additional profits from the current restaurants could cover the variables costs if the new restaurants were launched. Based on that, the owner had to make a decision about whether a new branch should be opened or whether they should just retain their restaurant in Gombak. Expected learning outcomes The learning objectives of using this case are as follows. 1. Knowledge enhancement: to help students in understanding the problems faced by a restaurant in expanding its market; to make students aware that a properly blended marketing mix is the key to business success and to broaden students’ views and understanding in targeting the proper market segment in formulating an effective marketing strategy. 2. Skills building: to be able to identify the best marketing strategic decisions to manage the restaurant business for its survival and to develop students’ ability to analyse the existing situation to come up with a viable and effective solution. 3. Attitudinal: to help the students to have intellectual openness in accepting different ways of finding solutions for a particular problem and to assist students in making the right move at the right time. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email [email protected] to request teaching notes. Subject code CSS 8: Marketing.

Recommender Technologies and Emerging Applications

2019 ◽  
pp. 458-470
Author(s):  
Young Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Future Research ◽  
Research Topics ◽  
Emerging Trends ◽  
System Approaches ◽  
Age Of Information

This chapter presents a brief overview of the field of recommender technologies and their emerging application domains. The authors explain the current major recommender system approaches within a unifying model, discuss emerging applications of recommender systems beyond traditional e-commerce, and outline emerging trends and future research topics, along with additional readings in the area of recommender technologies and applications. They believe that personalized recommender technologies will continue to advance and be applied in a variety of traditional and emerging application domains to assist users in the age of information overload.

Toward Geospatial Collaborative Tourism Recommender Systems

2018 ◽  
pp. 212-248
Author(s):  
Zahra Bahramian ◽  
Rahim Ali Abbaspour ◽  
Christophe Claramunt
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
World Wide ◽  
Information Overload ◽  
Spatial Information ◽  
User Profile ◽  
User Preferences ◽  
Research Issues ◽  
The World ◽  
Information Finding

Tourism activities are highly dependent on spatial information. Finding the most interesting travel destinations and attractions and planning a trip are still open research issues to GIScience research applied to the tourism domain. Nowadays, huge amounts of information are available over the world wide web that may be useful in planning a visit to destinations and attractions. However, it is often time consuming for a user to select the most interesting destinations and attractions and plan a trip according to his own preferences. Tourism recommender systems (TRSs) can be used to overcome this information overload problem and to propose items taking into account the user preferences. This chapter reviews related topics in tourism recommender systems including different tourism recommendation approaches and user profile representation methods applied in the tourism domain. The authors illustrate the potential of tourism recommender systems as applied to the tourism domain by the implementation of an illustrative geospatial collaborative recommender system using the Foursquare dataset.

An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

scholarly journals A Deep Learning-Based Course Recommender System for Sustainable Development in Education

2021 ◽  
Vol 11 (19) ◽  
pp. 8993
Author(s):  
Qinglong Li ◽  
Jaekyeong Kim
Keyword(s):  
Deep Learning ◽  
Online Education ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Course Content ◽  
User Behaviors ◽  
Real World Datasets ◽  
High Level

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods.

scholarly journals Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

2021 ◽  
Vol 11 (24) ◽  
pp. 11890
Author(s):  
Silvana Vanesa Aciar ◽  
Ramón Fabregat ◽  
Teodor Jové ◽  
Gabriela Aciar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
User Experiences ◽  
Relevant Feedback ◽  
Filtering Method

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.

scholarly journals Crowdsourced Recommender System

2018 ◽  
Author(s):  
Mario Mallia Milanes ◽  
Matthew Montebello
Keyword(s):  
Web 2.0 ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Relevant Information ◽  
Learning Systems ◽  
Learning Material ◽  
Timely Manner ◽  
Online Learners ◽  
E Learning

The use of artificially intelligent techniques to overcome specific shortcomings within e-learning systems is a well-researched area that keeps on evolving in an attempt to optimise such resourceful practices. The lack of personalization and the sentiment of isolation coupled with a feeling of being treated like all others, tends to discourage and push learners away from courses that are very well prepared academically and excellently projected intellectually. The use of recommender systems to deliver relevant information in a timely manner that is specifically differentiated to a unique learner is once more being investigated to alievate the e-learning issue of being impersonal.  The application of such a technique also assists the learner by reducing information overload and providing learning material that can be shared, criticized and reviewed at one’s own pace. In this paper we propose the use of a fully automated recommender system based on recent AI developments together with Web 2.0 applications and socially networked technologies. We argue that such technologies have provided the extra capabilities that were required to deliver a realistic and practical interfacing medium to assist online learners and take recommender systems to the next level.

Ethics for Knowledge Resource Centers in the Era of Information Overload

2020 ◽  
pp. 159-178
Author(s):  
J. John Jeyasekar ◽  
Aishwarya V. ◽  
Usharani Munuswamy
Keyword(s):  
Information Science ◽  
Information Overload ◽  
Code Of Ethics ◽  
Information Service ◽  
Hippocratic Oath ◽  
Ethical Code ◽  
Moral Code ◽  
Professional Body ◽  
Set Up ◽  
The Right

Every professional body has a moral code for the conduct of its members. For example, physicians follow the oldest Hippocratic Oath for their professional code of ethics. Every member of the profession should adhere to their ethical code. Eventually, in the era of information overload millions of Terabyte information is found in the web. The e-formats of the information pose challenges to its user. The information service providers have a moral responsibility of providing the right information to the right user, in the right form. The knowledge workers have an added social responsibility in the democratic set-up. The ALA, CILIP, and some other professional bodies have their own ethical code. However, many developing nations do not have such code. This chapter discusses what ethics is, and its relevance to information science professionals. In addition, it gives a glimpse of the various ethical codes available and formulates a set of codes for information producers, re-packers, and seekers.

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