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Method of Item Recommendation and Rating Based on Effort

IP.com Disclosure Number: IPCOM000240631D
Publication Date: 2015-Feb-13
Document File: 6 page(s) / 87K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a method and system to recommend and rate items on e-commerce sites based on the historical levels of effort that information contributors spent researching the item. Information contributors that spend more time and extend greater effort in researching an item are expected to produce more reliable product recommendations and ratings, which subsequent shoppers can use to assist purchasing decisions.

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Method of Item Recommendation and Rating Based on Effort

Users of the Internet are overloaded with a large amount of information. A user often has difficulty determining which data is more credible and relevant. For example, in an e-shopping process, a user may receive thousands of search results, which may look similar upon initial review. A user needs to look at more product details. A recommendation engine may provide other options for the shopper; however, if the contributors of the data used by the recommendation engine do not spend enough time in research , then the recommendation results may not be of high quality. For example, statistics for "top ranked item" might be skewed by people who "liked" the picture of the item on the site, as opposed to the users wrote a thoughtful review after doing a deeper research about different available options. Making decision without enough research or based on information from those who did not do enough research, might ultimately result in a dissatisfied consumer.

Other users tend to do more research before making choices. This can be a time consuming process. The information provided by the users who put a lot of effort into researching a product can benefit other users , but often the effort factor is neglected in the current technologies of item rating and recommendation. Only a few users go to the effort to do more research; thus, the data from those users does not significantly influence statistics if the statistics do not distinguish data based on effort .

The novel contribution is a system and method of item rating and recommendation based on the level of effort that contributors of the data put into the research and rating. Indicators of a greater level of research effort include (but are not limited to):

The number of related items the person viewed The number of related search results the person reviewed The length of time that the person spent on related pages Comments made on related items, Status related to the item that the person has updated on a social network website

Related browsing and searches done on external sites (discovered from the cookies in the user's browser)

After the system calculates the effort related to an item, it uses the result to rate or recommend the item. The idea is that information contributors that spend more time and extend greater effort in researching an item are expected to produce more reliable product recommendations and ratings, which subsequent shoppers can use to assist purchasing decisions. The provided rating and recommendation information can help reduce research time for people who meant to spend more time in research , and can help reduce the chance that people who do not make the effort to do further research choose unsuitable items .

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The system has modules of activity collection, effort calculation, and recommendation and rating calculation.

Figure 1: System diagram

The activity collection...