Happy Halloween! This Friday is the
Halloween, festival encounters with weekends. It is such a fantastic
opportunity for people to relax themselves. Actually, there are so many methods
for people to relax. For instance, watching movies in the theatre, enjoying a
banquet in the restaurant, playing sports games with friends and so on. For the
most of the time, we spend much time considering, “which method is the most
appropriate for me at present”. I believe this is a common phenomenon in our
daily lives. Generally speaking, when we have to make choices, if there are no
thoughtful recommendations, it is challenging for people to select a perfect
choice.
So here comes the problem, is there any
reliable method for people to make correct choice? In other word, we are
looking for some scientific recommendation mechanisms, which are able to
recommend things or persons which are perfectly suitable for us. During the
last two weeks, on Prof. Rosanna’s class, we are actually focusing on this
issue.
The concept of “recommendation mechanism”
refers to recommender systems actually. They are some specific computational solutions
which use computer to process the huge amount of information and do the
filtering for users. They are able to analyze the tastes and preferences of
different users, except from taking care of people, these systems also pay
attention to items. They have the capability of analyzing the characteristics
of different items. Finally, they will generate personalized recommendation
based on users’ past activities and feedback.
Several recommendation methods are
mentioned in the class. Let us take the content-based recommendation (CBR) as
an example. If I am the recommendation service provider. When someone asks me
for assistance, it is a wise choice to check this client’s history. Namely, to
find out the products and services he had received. In most cases, recommend
items which are similar to his history will not cause problems. Nevertheless,
one person’s past activities belong to personal privacy. It is difficult or
even illegal to sniff any information about that. Under this
circumstance, it is necessary to transfer our attention from items to users.
Here, another recommendation method is adopted. Collaborative filtering (CF).
When we are doing research on users themselves, several approaches may be
applied. For instance, grouping similar users, generating recommendation
reports basing on user-item interactions rather than users or items themselves.
Afterwards, lots of mathematical derivations are made in the class. Franking
speaking, they look complex and sophisticated, more time is needed for me to
make further research on them. In a word, in spite of these phenomena about
having-difficulty-in-choosing look normal in our daily social lives, there also
exist a lot of profound theories behind them.
Hello November, please be good!
Recommender systems become more and more important in recent years since we have to make many decisions every day. It is also one of the reasons why recommender systems are widely adopted in many websites. Maybe we can learn it together and share our thoughts with each other.
回复删除That is true, usually, we encounter these kinds of problems, wish Rosanna's class will teach us how to cope with them. :-)
删除In most cases, the Recommendation Systems will return an appropriate reasult but sometimes it may mislead the Choice-Phobia subjected to the accuracy of this system. Therefore, I don't think we should rely on the Recommendation Systems to make choices for us.
回复删除Your idea is unique. I totally agree with you, recommendation system is a kind of tools after all. We cannot rely on them badly.
删除1 dilemma of commendation system may be the efficience.Some guys with crazy ideas should be kick out the database as to get decent result...
回复删除Choice-phobia is a symptoms that many people have. A lot of them even don't really know what themselves want. If we can discover an effective way to help the majority solve the choice-phobia problem. It may be a huge opportunity!
回复删除Actually, the recommending system is everywhere in our daily life. When we go shopping the SA will recommend based on the information she receive from us. When designing the algorithm, we need to think deeply about the input factors and how to group those factors.
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