When you step into SASA and ask the sales
for a foundation make-up, it is quite possible for the sales to suggest you
with another make-up product, for instance, the blusher or BB cream. This is a
very typical recommender case in our daily life.
With the development of internet services, all
kinds of e-commerce platforms could not wait to introduce the recommender
system into their online shops. On one hand, many statistic reports have shown
that the recommender can be really helpful in raising the sales volume. On the
other hand, as an average customer exposed to over hundreds or even thousands
of choices, the recommender system is a time-saving tool for me when shopping.
Since in lecture 9&10 of social media
analytics, many algorithms for recommender have been introduced, in my fourth
post (the last post of this course), I am trying to compare the differences
between user-based collaborative filtering (UserCF) and item-based
collaborative filtering (ItemCF).
The main thought of UserCF is to find out
the users with the similar opinion or behaviors towards the same items first. The
items liked by the user group will be recommended to this user next. For
example, user A is interested in product X1, X2 and Y1 while user B is
interested in X1, X3 and Y2. According to UserCF algorithm, user A and user B
are regarded to have similar preferences, therefore the system may recommend Y1
to user B while recommend Y2 to user A. Figure-1 could be an application of
UserCF with a large possibility.
Figure-1
On contrary, the idea of ItemCF is to find
out the similar items first. When the user is selecting one item, the similar
item can be recommended. The judgment criterion is of the similarity is mainly
by the amount of users who hold the same attitude towards the two items. For
instance, if the item C and item D are liked by a large amount of users, the
system then will regards them as similar items. When user is looking at item C,
item D will be recommended to him or her. The related recommending (相关推荐) could be
implemented by ItemCF from my understanding.
Figure-2
So, will you agree or disagree with me?
You have combined theoretical knowledge with modern people's current dilemma, your article must be useful to people who are choice-phobia!
回复删除Hi,dear.Your blog is useful to me not only because I'm a choice-phobia,but also for your clear and concise expression.You really have deep understanding about UserCF and ItemCF.The example you give at beginning is attractive.
回复删除I agree with you about UserCL and ItemCL. You summarize it simply and clearly. and this idea is really similar to the some computer algorithms to extract feature out of object and classified to same category.
回复删除I often received the email from Amazon with recommendation books and always think Amazon knows me more than I do. The recommendation system of Amazon is really amazing. Based on the big data accummulated through billions of users and several years, it could precisely analyze the preference of each individual. I think Amazon might be the best player of recommender system.
回复删除You really done a great job on the recommender system. For the grouping of the user, I think the system will try to group them with their interest f. People will similar interest will properly like the same things.
回复删除Thank you for your sharing. Your description of the collaborative filtering is clear and that is the idea of collaborative filtering. well done.
回复删除these two kinds of recommender systems are quite common use in many e-commerce company website and surely some other online shopping website, for me, I think both of the two method is great and in fact I also compared with many other recommended result before I made my final choice, but your research on this can give us more guidance.
回复删除Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general.Thank you for your sharing.
回复删除Recommender systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things he has never experienced.
回复删除oh, you are the third person who analyzes the recommending system I have read. the former two persons are chenlina and wuzeyu. But I think it is understandable since this is really connected to our subject of course.
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