Deriving item features relevance from collaborative domain knowledge

An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based filtering). Collaborative algorithms have been proven to achieve better recommendation quality then content based algorithms in a variety of scenarios, being more effective in modeling user behaviour. However, they can not be applied when items have no interactions at all, i.e. cold start items. Content based algorithms, which are applicable to cold start items, often require a lot of feature engineering in order to generate useful recommendations. This issue is specifically relevant as the content descriptors become large and heterogeneous. The focus of this paper is on how to use a collaborative models domain-specific knowledge to build a wrapper feature weighting method which embeds collaborative knowledge in a content based algorithm. We present a comparative study for different state of the art algorithms and present a more general model. This machine learning approach to feature weighting shows promising results and high flexibility

Öneri sistemleri ve E- ticarette öneri sistemlerinin kullanımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Anahtar kelimeler: Öneri sistemleri, içerik bazlı öneri sistemleri, işbirlikçi öneri sistemleri, hibrit içerik sistemleri, e-ticaret. İnternet üzerinden alışveriş, artık hayatımızda alışveriş alışkanlıklarımızı büyük bir ölçüde değiştirdi ve değiştirmeye de devam ediyor. Günün her saatinde her kategoride ürüne ulaşmamız için ihtiyacımız olan sadece internetle birlikte bir bilgisayar, akıllı telefon veya bir tablet. Bu sayede, ihtiyaç duyduğumuz veya almayı düşündüğümüz herhangi bir ürün için farklı alternatiflere, farklı kalite ve fiyatlara ulaşmamız artık çok kolay. Yaşadığımız ülkenin hatta dünyanın herhangi bir noktasından kapımıza kadar ürünün teslimini sağlayabiliyoruz. Öneri sistemleri, alınması düşünülen ürünler için aynı veya benzer ürünleri önceden almış başka müşterilerin yorum, değerlendirme ve oylarının da yardımıyla alternatif seçenekler veya tamamlayıcı başka ürünler önererek yapılacak alışverişi çok kolaylaştırmaktadır. Bu sayede zamandan tasarruf edilmesi, ihtiyaca daha yakın ürünlerin incelenmesi sağlanmış olur.Keywords: Recommendation systems, content-based recommendation systems, collaborative recommendation systems, hybrid recommendation systems, e-commerce Online shopping has changed our shopping habits to a great extent now and continues to change. In order to reach a product in any categories at any time of the day, we just need to have a computer, a smartphone or a tablet which has Internet connection. Thus, it becomes easy to reach different alternatives, different quality and prices for any product that we need or want to buy. Thanks to Internet, we can deliver the product from any place of the world. Suggestion systems make shopping much easier for the products to be bought by offering alternative options or complementary products with the help of the comments, evaluations and votes of other customers who have already bought the same or similar products for the intended products. Therefore, they help save time and examine the products that are most needed

Deriving item features relevance from collaborative domain knowledge

An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based filtering). Collaborative algorithms have been proven to achieve better recommendation quality then content based algorithms in a variety of scenarios, being more effective in modeling user behaviour. However, they can not be applied when items have no interactions at all, i.e. cold start items. Content based algorithms, which are applicable to cold start items, often require a lot of feature engineering in order to generate useful recommendations. This issue is specifically relevant as the content descriptors become large and heterogeneous. The focus of this paper is on how to use a collaborative models domain-specific knowledge to build a wrapper feature weighting method which embeds collaborative knowledge in a content based algorithm. We present a comparative study for different state of the art algorithms and present a more general model. This machine learning approach to feature weighting shows promising results and high flexibility