In this paper, our work is relates to collaborative filtering
with implicit feedback and learning to rank.
Li  analyzed the sales records in the current tea leaves sales system by combining Hadoop distributed system with the traditional collaborative filtering
algorithm to obtain the recommendation rules which could satisfy the preference of customer and help users find the tea leaves they needed.
They utilized both the learners' learning styles and the knowledge levels to elicit trust values among learners and incorporate them with collaborative filtering
techniques to suggest trusted learning resources.
In the other two relationships, the relationship weightings are calculated using the collaborative filtering
method, which are denoted by [mathematical expression not reproducible] .
The basic idea of the user-based collaborative filtering
algorithm is the observation that the level of interuser similarity increases with the number of locations registered by the two users.
As shown in Figure 1, the user [u.sub.a], [u.sub.a] [member of] U is called the active user for whom the task of the collaborative filtering
algorithm is to find an item likeliness that can be in two forms.
To alleviate this difficulty, recently a number of cross-domain collaborative filtering
(CDCF) methods have been proposed .
The report cites collaborative filtering
technologies and recommendation engines as important in delivering low-price guarantees as well as cross-sells and upsells that can increase average order value.
This work concentrates on the situation where the clear element portrayal of items is inaccessible, a setup that is like collaborative filtering
. With a specific end a client's references from his/her reaction to just few sets pairwise correlations and propose to use the comparison of pairwise made by many group clients, an issue suggest to as group ranking.
Combining Collaborative Filtering
with Personal Agents for Better Recommendations.