Interactive Product Catalog for M-Commerce

2007 ◽  
pp. 345-351
Author(s):  
S. Guan ◽  
Y. Tay
Keyword(s):  
Mobile Device ◽  
Recommender System ◽  
Personal Digital Assistant ◽  
Detection Technique ◽  
User Preference ◽  
Interactive Product ◽  
Product Appearance

We propose a product catalog where browsing is directed by an integrated recommender system. The recommender system is to take incremental feedback in return for browsing assistance. Product appearance in the catalog will be dynamically determined at runtime based on user preference detected by the recommender system. The design of our hybrid m-commerce catalog-recommender system investigated the typical constraints of m-commerce applications to conceptualize a suitable catalog interface. The scope was restricted to the case of having a personal digital assistant (PDA) as the mobile device. Thereafter, a preference detection technique was developed to serve as the recommender layer of the system.

Interactive Personalized Catalogue for M-Commerce

2008 ◽  
pp. 218-228
Author(s):  
Sheng-Uei Guan ◽  
Yuan Sherng Tay
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Recommender System ◽  
User Interfaces ◽  
Personal Digital Assistant ◽  
User Preference ◽  
Screen Size ◽  
Size Constraints ◽  
Small Form Factor ◽  
Small Screen

M-commerce possesses two distinctive characteristics that distinguish it from traditional e-commerce: the mobile setting and the small form factor of mobile devices. Of these, the size of a mobile device will remain largely unchanged due to the tradeoff between size and portability. Small screen size and limited input capabilities pose a great challenge for developers to conceptualize user interfaces that have good usability while working within the size constraints of the device. In response to the limited screen size of mobile devices, there has been unspoken consensus that certain tools must be made available to aid users in coping with the relatively large volume of information. Recommender systems have been proposed to narrow down choices before presenting them to the user (Feldman, 2000). We propose a product catalogue where browsing is directed by an integrated recommender system. The recommender system is to take incremental feedback in return for browsing assistance. Product appearance in the catalogue will be dynamically determined at runtime based on user preference detected by the recommender system. The design of our hybrid m-commerce catalogue recommender system investigated the typical constraints of m-commerce applications to conceptualize a suitable catalogue interface. The scope was restricted to the case of having personal digital assistant (PDA) as the mobile device. Thereafter, a preference detection technique was developed to serve as the recommender layer of the system.

Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

scholarly journals User-Adaptive Key Click Vibration on Virtual Keyboard

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Seokhee Jeon ◽  
Hongchae Lee ◽  
Jiyoung Jung ◽  
Jin Ryong Kim
Keyword(s):  
Mobile Device ◽  
Tactile Feedback ◽  
User Preferences ◽  
User Preference ◽  
Performance Study ◽  
Mobile Environment ◽  
Virtual Keyboard ◽  
User Groups ◽  
Environment Study ◽  
Future Work

This study focuses on design of user-adaptive tactile keyboard on mobile device. We are particularly interested in its feasibility of user-adaptive keyboard in mobile environment. Study 1 investigates how tactile feedback intensity of the virtual keyboard in mobile devices affects typing speed and user preference. We report how different levels of feedback intensity affect user preferences in terms of typing speed and accuracy in different user groups with different typing performance. Study 2 investigates different tactile feedback modes (i.e., whether feedback intensity is linearly increased, linearly decreased, or constant from the centroid of the key, and whether tactile feedback is delivered when a key is pressed, released, or both pressed and released). We finally design and implement user-adaptive tactile keyboards on mobile device to explore the design space of our keyboards. We close by discussing the benefits of our design along with its future work.

Presenting Spatial Information on a Mobile Device: Differences in Workload and Performance

2007 ◽  
Vol 51 (4) ◽  
pp. 353-357
Author(s):  
Jim Nixon ◽  
Sarah Sharples ◽  
Mike Jackson
Keyword(s):  
Mobile Device ◽  
Spatial Information ◽  
Personal Digital Assistant ◽  
Floor Plan ◽  
Mental Demand ◽  
Shortest Route ◽  
Navigation Support ◽  
Floor Plans ◽  
And Performance ◽  
Completion Times

An experiment was conducted to study differences in workload and performance of participants when navigating a route. Participants used a mobile device to navigate a route in a building. Different types of representation were used: a paper floor plan and three representations presented on a personal digital assistant (PDA). In the PDA based conditions, an overview of the floor plan was presented in a picture viewer. Since the plan was much larger than the PDA screen, participants moved different parts of the plan into view using a stylus. Floor plans were also presented as a sequence of plan fragments on the PDA which were advanced by the user according to location. Results show significantly shorter route completion times for participants using the paper plan compared with the PDA support. Significant differences in workload, effort and mental demand were also found between the types of representation. The paper plan condition elicited the lowest levels of workload and the shortest route completion times. Implications for the design of location-based navigation support are discussed.

Investigation About Applicability of Personal Values for Recommender System

2012 ◽  
Vol 16 (3) ◽  
pp. 404-411 ◽  
Author(s):  
Shunichi Hattori ◽  
◽  
Yasufumi Takama
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Personal Values ◽  
User Preference

A recommender systemis a fundamental technique for finding information that is likely to be preferred by users among vast amounts of information. While existing recommender systems usually employ user preference or attributes of items to make recommendations, marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. This paper investigates the applicability of personal values in modeling items and users. The results of questionnaires show the feasibility of a recommender system based on personal values.

scholarly journals A Genetic-Based Pairwise trip Planner Recommender System

2021 ◽  
Author(s):  
Nunung Nurul Qomariyah ◽  
Dimitar Kazakov
Keyword(s):  
Recommender System ◽  
Evaluation Study ◽  
Preference Elicitation ◽  
User Preference ◽  
Elicitation Method ◽  
Massive Growth ◽  
Internet Users ◽  
Pairwise Preference ◽  
Personalized Recommender System ◽  
Trip Planner

Abstract The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

scholarly journals Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System

2019 ◽  
Vol 9 (10) ◽  
pp. 1992 ◽  
Author(s):  
Hui Liu ◽  
Yinghui Huang ◽  
Zichao Wang ◽  
Kai Liu ◽  
Xiangen Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Recommender System ◽  
Consumer Preferences ◽  
Explanatory Power ◽  
Marketing Research ◽  
User Preferences ◽  
User Preference ◽  
Data Driven ◽  
Product Categories

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research.

scholarly journals A Personalized Music Recommender System Using User Contents, Music Contents and Preference Ratings

2020 ◽  
Vol 07 (01) ◽  
pp. 77-92
Author(s):  
Ja-Hwung Su ◽  
Chu-Yu Chin ◽  
Yi-Wen Liao ◽  
Hsiao-Chuan Yang ◽  
Vincent S. Tseng ◽  
...  
Keyword(s):  
Recommender System ◽  
Mean Squared Error ◽  
Communication Technologies ◽  
User Preference ◽  
Acoustic Features ◽  
Rating Data ◽  
The Arts ◽  
Online Music ◽  
Preference Ratings ◽  
Better Than

Recently, the advances in communication technologies have made music retrieval easier. Without downloading the music, the users can listen to music through online music websites. This incurs a challenging issue of how to provide the users with an effective online listening service. Although a number of past studies paid attention to this issue, the problems of new user, new item and rating sparsity are not easy to solve. To deal with these problems, in this paper, we propose a novel music recommender system that fuses user contents, music contents and preference ratings to enhance the music recommendation. For dealing with problem of new user, the user similarities are calculated by user profiles instead of traditional ratings. By the user similarities, the unknown ratings can be predicted using user-based Collaborative Filtering (CF). For dealing with problems of rating sparsity and new items, the unknown ratings are initialized by acoustic features and music genre ratings. Because the unknown ratings are initially imputed, the rating data will be enriched. Thereupon, the user preference can be predicted effectively by item-based CF. The evaluation results show that our proposed music recommender system performs better than the state-of-the-arts methods in terms of Root Mean Squared Error.

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