Firstly, I want to analysis why should we use such recommendation systems. On the one hand,there are values for the customer such as finding things that are interesting,narrowing down the set of choices, helping us explore the space of options,discover new things,entertainment and so on . On the other hand,there are also advantages for providers such as Additional and probably unique personalized service for the customer, increasing trust and customer loyalty, Increasing sales, click trough rates, conversion etc,opportunities for promotion, persuasion. Obtaining more knowledge about customers and so on .
Secondly,I notice there are two basic architectures for a recommendation system:1.Content-Based systems focus on properties of items. Similarity of items is determined by measuring the similarity in properties2.CollaborativeFiltering systems focus on the relationship between users and items. Similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items.In a contentbased system, we must construct for each item a profile, which is a record or collection of records representing important characteristics of that item. In simple cases, the profile consists of some characteristics of the item that are easily discovered.
Lastly, I am going to introduce how to recommend items to users based on content.With profile vectors for both users and items, we can estimate the degree to which a user would prefer an item by computing the cosine distance between the user’s and item’s vectors.The random-hyperplane hashing techniques can be used to place (just) item profiles in buckets. In that way, given a user to whom wewant to recommend some items, we can apply the same two techniques – random hyperplanes and LSH – to determine in which buckets we must look for items that might have a small cosine distance from the user.
This week, the professor told things about machine learning, and I found that you have a good understand about what the teacher said, you said that there are two basic architectures for a recommendation system, which I am not so clear about it, could you tell me more about that, thanks very much.
回复删除Recommender system is a complex but important part of machine learning. It's very nice of you to have summarized the basic points of recommender system, which can be help us clarify our minds about this concept.
回复删除Thank you for your sharing.By reading through you blog ,I have known that recommender systems are proving to be a useful tool for addressing a portion of the information overload phenomenon from the Internet. Its evolution has accompanied the evolution of the web.
回复删除Thank you for your sharing.Content-based filtering methods are based on a description of the item and a profile of the user’s preference.In a content-based recommender system, keywords are used to describe the items; beside, a user profile is built to indicate the type of item this user likes.
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