A time-based approach to effective recommender systems using implicit feedback

Lee, Tong Queue; Park, Young; Park, Yong‐Tae

doi:10.1016/j.eswa.2007.06.031

Cited by 159 publications

(82 citation statements)

References 9 publications

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“…Research has shown [24] that taking into account time information, i.e., when an item was released and purchased or in our scenario a game was played, may further improve the accuracy of the collaborative filtering based recommender systems. We are currently extending our research on the recommendation of online social games by taking the game play dates into account for the conversion of gaming data to an implicit rating.…”

Section: Discussionmentioning

confidence: 99%

Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites

Leroux

Dhoedt

Demeester

et al. 2012

J. Comput. Sci. Technol.

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Since years, online social networks have evolved from profile and communication websites to online portals where people interact with each other, share and consume multimedia-enriched data and play different types of games. Due to the immense popularity of these online games and their huge revenue potential, the number of these games increases every day, resulting in a current offering of thousands of online social games. In this paper, the applicability of neighborhood-based collaborative filtering (CF) algorithms for the recommendation of online social games is evaluated. This evaluation is based on a large dataset of an online social gaming platform containing game ratings (explicit data) and online gaming behavior (implicit data) of millions of active users. Several similarity metrics were implemented and evaluated on the explicit data, implicit data and a combination thereof. It is shown that the neighborhood-based CF algorithms greatly outperform the content-based algorithm, currently often used on online social gaming websites. The results also show that a combined approach, i.e., taking into account both implicit and explicit data at the same time, yields overall good results on all evaluation metrics for all scenarios, while only slightly performing worse compared to the strengths of the explicit or implicit only approaches. The best performing algorithms have been implemented in a live setup of the online game platform.

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Section: Discussionmentioning

confidence: 99%

Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites

Leroux

Dhoedt

Demeester

et al. 2012

J. Comput. Sci. Technol.

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“…They proposed a latent factor algorithm that addresses the preferenceconfidence paradigm to tailor for implicit feedback recommendations. [12] incorporated temporal information, such as user purchase time and item launch time, to construct pseudo rating data from the user purchase information for collaborative filtering. Instead of simply assigning 1 to the purchased items, a rating function is defined that computes rating values based on the launch time and purchase time of items to reflect the user's preferences to achieve better recommendation accuracy.…”

Section: Related Workmentioning

confidence: 99%

Integrating Collaborative Filtering and Matching-based Search for Product Recommendations

Abdullah

Geva

2013

J. theor. appl. electron. commer. res.

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Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user's preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely RoundRobin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

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“…An example of explicit behaviors is that a customer tells us which products he like or dislike while implicit behaviors cannot demonstrate users' product preference directly. These implicit behaviors include user purchase patterns, web page visits and web browsing paths [1]. In addition, Lee et al propose that customers of online stores go through four main shopping steps: product impression, click-through patterns, basket placement and purchase [2].…”

Section: Introductionmentioning

confidence: 99%

Purchase Behavior Prediction in E-Commerce with Factorization Machines

Chen

Hou

Xiao

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYPurchase behavior prediction is one of the most important issues for the precision marketing of e-commerce companies. This Letter presents our solution to the purchase behavior prediction problem in E-commerce, specifically the task of Big Data Contest of China Computer Federation in 2014. The goal of this task is to predict which users will have the purchase behavior based on users' historical data. The traditional methods of recommendation encounter two crucial problems in this scenario. First, this task just predicts which users will have the purchase behavior, rather than which items should be recommended to which users. Second, the large-scale dataset poses a big challenge for building the empirical model. Feature engineering and Factorization Model shed some light on these problems. We propose to use Factorization Machines model based on the multiple classes and high dimensions of feature engineering. Experimental results on a real-world dataset demonstrate the advantages of our proposed method.

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A time-based approach to effective recommender systems using implicit feedback

Cited by 159 publications

References 9 publications

Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites

Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites

Integrating Collaborative Filtering and Matching-based Search for Product Recommendations

Purchase Behavior Prediction in E-Commerce with Factorization Machines

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