Peer-Based Recommendations in Online B2C E-Commerce: Comparing Collaborative Personalization and Social Network-Based Personalization

Li, Siyuan; Karahanna, Elena

doi:10.1109/hicss.2012.469

Cited by 19 publications

(17 citation statements)

References 15 publications

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“…Basic idea of collaborative filtering is to find neighbours of target user and then predict target user interests. Details of personalization approaches with the comparative study of collaborative personalization and social network-based personalization can be found in [16]. Their findings suggest that when the personalized recommendations are generated outside the category on which collaborative similarity matching occurred, collaborative personalization is significantly less accurate than social network-based personalization.…”

Section: ) Collaborative Filtering Approachesmentioning

confidence: 94%

Re-ranking for personalization using concept hierarchy in DL environment

Potey

Pawar²,

Sinha

2013

2013 15th International Conference on Advanced Computing Technologies (ICACT)

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In Digital Library (DL) system, users interact with the system to search for books or research papers. Users can search through metadata or search for information in the pages by querying using keywords. In both cases, a huge amount of results are returned; however, the relevant ones to the user are not often amongst the top few. Re-ranking of the search results based on the user's interest has received wide attention in information retrieval. This work presents extending conventional search engine to searching digital library data of user's interests. The proposed system improve information access by building knowledge about a user, acquired using the user's interaction with the system, in order to customize information access. User profiling is done using a hybrid approach by taking into consideration login details and click-through data. This system mapping framework automatically maps Dmoz Open Directory Project (ODP) topics to users' interests and takes advantage of manually edited data available in ODP, to categorize and personalize search results, according to user interests. Reranking of the results is done based on user interests. This makes it easy to find relevant DL pages faster than normal search engines. Performance has been evaluated for online DL systems. System's performance improves by 16.55% if the average per query is calculated and approximately 10% if per user average is calculated over baseline (Google CSE) after re-ranking.

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Section: ) Collaborative Filtering Approachesmentioning

confidence: 94%

Re-ranking for personalization using concept hierarchy in DL environment

Potey

Pawar²,

Sinha

2013

2013 15th International Conference on Advanced Computing Technologies (ICACT)

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“…It has been observed that the activity plays an inevitable and incredible role in reducing potential risks, which are usually occurred due to increased complexities. Li and Karahanna (2012) have asserted that the entire process can be easily conducted by the help of query breaking module. However, this module also possesses some issues, in terms of identifying adequate LECO (local e-catalog ontologies).…”

Section: A) Query Breaking Modulementioning

confidence: 99%

“…In this scenario, e-businesses have to implement and integrate high-tech systems, in order being ease and convenience for e-commerce users, in terms of searching their required products and services. It has been claimed by Li and Karahanna (2012) that database queries are found to be one of the integrated and valuable methods, which assists the users in accessing required products, in considerably massive and sophisticated databases.…”

Section: Significance Of Researchmentioning

confidence: 99%

“…It has been assessed that the concept of e-commerce has gained tremendous popularity with the emergence of innovative and advanced technological tools. According to Li and Karahanna (2012), e-commerce or electronic commerce can be understood as the trading of services or products, by using computer networks, usually internet. It has been assessed that the functions or operations of e-commerce draws on different technologies, including automated data collection systems, inventory management systems, EDI (electronic data interchange), electronic funds transfer ,online transaction processing, supply chain management ,internet marketing, and mobile commerce.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Framework with Advanced Study to Incorporate the Searching of E-Commerce Products Using Modernization of Database Queries

Muntjir¹,

Siddiqui²

2016

ijacsa

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Abstract-This study aims to inspect and evaluate the integration of database queries and their use in e-commerce product searches. It has been observed that e-commerce is one of the most prominent trends, which have been emerged in the business world, for the past decade. E-commerce has gained tremendous popularity, as it offers higher flexibility, cost efficiency, effectiveness, and convenience, to both, consumers and businesses. Large number of retailing companies has adopted this technology, in order to expand their operations, across of the globe; hence they needs to have highly responsive and integrated databases. In this regard, the approach of database queries is found to be the most appropriate and adequate techniques, as it simplifies the searches of e-commerce products.

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confidence: 99%

Location based context aware recommender system through user defined rules

Sharma

Kaur

2015

International Conference on Computing, Communication &Amp; Automation

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Recommender systems are a subclass of information filtering system and are widely used in the ecommerce domain [13]. They filter huge amount of data to provide personalized recommendations on services or products to users. Most of the existing approaches to develop a recommender system do not take into account contextual information such as weather, day, time, distance and location to provide recommendations. This paper proposes a location based context aware recommender system [9] that uses a ranking function to provide top-k recommendations to the user. The contextual data is defined by the users in the form of rules and RuleML [1] is chosen as a rule based language. When an active user needs recommendations of nearby places then contextual data in the user-defined RuleML rules is extracted, evaluated and top-k recommendations of nearby places based on the ranking function are presented to the user.

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Peer-Based Recommendations in Online B2C E-Commerce: Comparing Collaborative Personalization and Social Network-Based Personalization

Cited by 19 publications

References 15 publications

Re-ranking for personalization using concept hierarchy in DL environment

Re-ranking for personalization using concept hierarchy in DL environment

An Enhanced Framework with Advanced Study to Incorporate the Searching of E-Commerce Products Using Modernization of Database Queries

Location based context aware recommender system through user defined rules

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