دانلود و نمایش مقالات مرتبط با فیلترسازی مشارکتی::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - فیلترسازی مشارکتی

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Assessing new correlation-based collaborative filtering approaches for binary market basket data
بررسی دیدگاههای فیلترسازی مشارکتی مبتنی بر همبستگی برای داده های دوتایی سبد خرید-2018
Binary market basket data are common in marketing settings. Pearson correlation-based approaches have applied to this kind of data in the past. This research assessed the principles of this approach research identifies some related problems. By resolving the problems, I develop new Pearson correlation-based approaches that use separated terms and separated terms with proportions. The experimental results show that the approaches perform better than the existing ones for market basket data in terms of top-N accuracy.
keywords: Binary market basket data |Collaborative filtering |Pearson correlation |Top-N accuracy
مقاله انگلیسی
2 Recommendation system development for fashion retail e-commerce
تحقیق روی سیستم توصیه برای تجارت الکترونیک خرده فروشی مسایل مد-2018
This study presents a real-world collaborative filtering recommendation system implemented in a large Korean fashion company that sells fashion products through both online and offline shopping malls. The company’s recommendation environment displays the following unique characteristics: First, the company’s online and offline stores sell the same products. Second, fashion products are usually seasonal, so customers’ general preference changes according to the time of year. Last, customers usually purchase items to replace previously preferred items or purchase items to complement those already bought. We propose a new system called K-RecSys, which extends the typical item-based collaborative filtering algorithm by reflecting the above domain characteristics. K-RecSys combines online product click data and offline product sale data weighted to reflect the online and offline preferences of customers. It also adopts a preference decay function to reflect changes in preferences over time, and finally recommends substitute and complementary products using product category information. We conducted an A/B test in the actual operating environment to compare K-RecSys with the existing collaborative filtering system implemented with only online data. Our experimental results show that the proposed system is superior in terms of product clicks and sales in the online shopping mall and its substitute recommendations are adopted more frequently than complementary recommendations.
keywords: Collaborative filtering |E-commerce |Fashion industry |Recommendation system
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 4972 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 4972 :::::::: افراد آنلاین: 66