کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
384451 660847 2012 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Product recommendation with temporal dynamics
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Product recommendation with temporal dynamics
چکیده انگلیسی

In many E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Diapers.com within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms produce recommendation lists similar to items that the target user has accessed before (content filtering), or compute recommendation by analyzing the items purchased by the users who are similar to the target user (collaborative filtering). Such recommendation paradigms cannot effectively resolve the situation with a life cycle, i.e., the need of customers within different stages might vary significantly. In this paper, we model users’ behavior with life cycles by employing hand-crafted item taxonomies, of which the background knowledge can be tailored for the computation of personalized recommendation. In particular, our method first formalizes a user’s long-term behavior using the item taxonomy, and then identifies the exact stage of the user. By incorporating collaborative filtering into recommendation, we can easily provide a personalized item list to the user through other similar users within the same stage. An empirical evaluation conducted on a purchasing data collection obtained from Diapers.com demonstrates the efficacy of our proposed method.


► We define a new class of recommendation problem, named recommendation with stage (RwS).
► We introduce a taxonomy-oriented approach to model a user’s long-term preference.
► We propose to capture a user’s preference both on the item level and also on semantic level.
► We segment the user’s consumption history and focus more on the recent or current stages.
► We investigate different collaborative filtering algorithms for product recommendation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 16, 15 November 2012, Pages 12398–12406
نویسندگان
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