| dc.description.abstract | Recommendation systems are crucial in suggesting items to users which increase user
experiences. Commonly, recommender systems are based on content-based and collaborative
filtering strategies. However, the two mentioned methods depend on user profiles and item
preferences as well as on a broad history of client inclinations. Such strategies confront a
number of challenges: counting the cold-start issue [18, 20] in frameworks characterized by
security concerns, and settings where the extend of markers speaking to client interface is
restricted. Therefore, recommender systems that based on Pairwise Association Rules are
portrayed as the recommender calculation that builds a demonstrate of collective inclinations
independently of individual client interface which reduce the relying on knowledge of content
[12] and does not require a complex framework of evaluations. The execution of the algorithm
is analyzed on a huge value-based information set produced by real-world dietary admissions
review application. By applied PAR to the assessment, performed on a huge information set of
genuine dietary recalls, the result clearly proved that the new algorithm is definitely performed
way better compares to other approaches. Nevertheless, user experiences are considered to be
better day by day, this thesis aims to develop a more efficient version of Pairwise Association
Rules, which focus on re-ranking the order of suggestions by applying rating of the item to the
calculation. The expected result of proposed algorithm will prove that the new approach gives
approximate value of precision and recall to the original PAR but the quality of each prediction
is better for users. | en_US |
