Tomorrow would be a great day for majority fans of online shopping for the coming activity of global free shipping on Nov.11th, so-called "Double-eleven". I totally believe many people have awaited this day for quite a long time. One of my roommates is exactly in such a group. He has kept surfing the Internet several hours a day to search items and see the comments since one week ago. He even doesn’t know what he wants to buy but still searches and searches. Actually, what really matters to him is just the discount and the bonus of free shipping, if he doesn't buy anything, the feeling of losing something will make him upset. "Perhaps it will be useful someday.", "Well, what is 'it' you'd like to mean?", "I'm right search for it."
Nowadays, my roommate's behavior doesn't seem too strange in our daily life. When we want to make a decision, there are so many information we can refer to which can make us confused so easily leading to a harder decision. The culprit responding to this is the overload of information, which is one of the negative impacts of too rich information in this age. It becomes harder and harder for us to react as sensitive as information dissemination as well as many useless information associated with what we really want also reduce our efficiency.
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Because of this situation, when we make decisions, we'd like to ask people something in common with us for advice. Their advice depending on their own experience helps us from making a bad decision.
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We can also ask someone else for a hand, which is a computational system called "recommender system". It works to analyze your behavior in the past and give you suggestion this time. There are three most popular method to design recommender system, that are collaborative filtering, content-based filtering and hybrid recommender system. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Another common approach when designing recommender systems is content-based filtering. 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. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past (or is examining in the present). In particular, various candidate items are compared with items previously rated by the user and the best-matching items are recommended. Recent research has demonstrated that a hybrid approach, combining collaborative filtering and content-based filtering could be more effective in some cases. Hybrid approaches can be implemented in several ways: by making content-based and collaborative-based predictions separately and then combining them; by adding content-based capabilities to a collaborative-based approach (and vice versa); or by unifying the approaches into one model. Several studies empirically compare the performance of the hybrid with the pure collaborative and content-based methods and demonstrate that the hybrid methods can provide more accurate recommendations than pure approaches. With the help of this smart friend, our life can return to its easy state as it used to be.
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Perhaps my roommate finally feels boring to this endless searching, so he decides to ask for some advice to his smart friend, just standing before him. "What would prefer 'it' is?", "As a photographer, a fit light stand undoubtedly will make you more professional". "Maybe you are right. I'll looking for one.". Great, I'm finally no need to lift the spotlight for him any more.
