27,406 research outputs found
Empirical Findings On Persuasiveness Of Recommender Systems For Customer Decision Support In Electronic Commerce
More and more companies are making online presence by opening online stores and providing customers with company and products information but the overwhelming amount of information also creates information overload for the customers. Customers feel frustrated when given too many choices while companies face the problem of turning browsers into actual buyers. Online recommender systems have been adopted to facilitate customer product search and provide personalized recommendation in the market place. The study will compare the persuasiveness of different online recommender systems and the factors influencing customer preferences. Review of the literature does show that online recommender systems provide customers with more choices, less effort, and better accuracy. Recommender systems using different technologies have been compared for their accuracy and effectiveness. Studies have also compared online recommender systems with human recommendations 4 and recommendations from expert systems. The focus of the comparison in this study is on the recommender systems using different methods to solicit product preference and develop recommendation message. Different from the technology adoption and acceptance models, the persuasive theory used in the study is a new perspective to look at the end user issues in information systems. This study will also evaluate the impact of product complexity and product involvement on recommendation persuasiveness. The goal of the research is to explore whether there are differences in the persuasiveness of recommendation given by different recommender systems as well as the underlying reasons for the differences. Results of this research may help online store designers and ecommerce participants in selecting online recommender systems so as to improve their products target and advertisement efficiency and effectiveness
Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization
Peer reviewedPostprin
A Comparative Study of Collaborative Filtering in Product Recommendation
Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation systemās sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithmsā metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluationās precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities.Ā Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PD
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
Albeit, the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be straightforwardly transformed to the implicit case if
scalability should be maintained. There are few if any implicit feedback
benchmark datasets, therefore new ideas are usually experimented on explicit
benchmarks. In this paper, we propose a generic context-aware implicit feedback
recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor
factorization learning method that scales linearly with the number of non-zero
elements in the tensor. The method also allows us to incorporate diverse
context information into the model while maintaining its computational
efficiency. In particular, we present two such context-aware implementation
variants of iTALS. The first incorporates seasonality and enables to
distinguish user behavior in different time intervals. The other views the user
history as sequential information and has the ability to recognize usage
pattern typical to certain group of items, e.g. to automatically tell apart
product types or categories that are typically purchased repetitively
(collectibles, grocery goods) or once (household appliances). Experiments
performed on three implicit datasets (two proprietary ones and an implicit
variant of the Netflix dataset) show that by integrating context-aware
information with our factorization framework into the state-of-the-art implicit
recommender algorithm the recommendation quality improves significantly.Comment: Accepted for ECML/PKDD 2012, presented on 25th September 2012,
Bristol, U
In CARSWe Trust: How Context-Aware Recommendations Affect Customersā Trust And Other Business Performance Measures Of Recommender Systems
Most of the work on Context-Aware Recommender Systems (CARSes) has focused on demonstrating that the contextual information leads to more accurate recommendations and on developing efficient recommendation algorithms utilizing this additional contextual information. Little work has been done, however, on studying how much the contextual information affects purchasing behavior and trust of customers. In this paper, we study how including context in recommendations affects customersā trust, sales and other crucial business-related performance measures. To do this, we performed a live controlled experiment with real customers of a commercial European online publisher. We delivered content-based recommendations and context-aware recommendations to two groups of customers and to a control group. We measured the recommendationsā accuracy and diversification, how much customers spent purchasing products during the experiment, quantity and price of their purchases and the customersā level of trust. We aim at demonstrating that accuracy and diversification have only limited direct effect on customersā purchasing behavior, but they affect trust which drives the customer purchasing behavior. We also want to prove that CARSes can increase both recommendationsā accuracy and diversification compared to other recommendation engines. This means that including contextual information in recommendations not only increases accuracy, as was demonstrated in previous studies, but it is crucial for improving trust which, in turn, can affect other business-related performance measures, such as companyās sales.Polytechnic of Bari, Italy; NYU Stern School of Busines
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
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