A collaborative filtering recommender system for infrequently purchased product using slope-one algorithm and association rule mining - Zolhani, Nur Azleen (2015)
Nowadays, tourism industry are actively being utilised in generating a state or country income. In order to attract tourist from all over places, information conveyance is important. Traditionally, people travels to certain places based on oral recommendation by families and friends. Now, people tends to go travel based on reviews that are read from blogs and websites. But, this leads to overflow of unfiltered information. In order to effectively recommending places to travel for tourist, recommendation engine are being developed. Most recommendation engine has suffice information to make recommendation for example Amazon.com recommendation and Google.com recommendation. Meanwhile, in tourism it is quite challenging in making recommendation because hotels are occasionally being booked or purchased by consumer. This is due to the fact that travelling are expensive and time consuming. This project implement the collaborative filtering using slope-one algorithm and also implement association rule mining in recommending hotels for tourist. This recommender system uses slope-one algorithm whereby it accumulate and takes into account of the difference in popularity. The objective of this project to study different types of recommendation techniques for infrequently purchased products and to investigate technique and dataset that are suitable to implement in recommending infrequently purchased products. As a conclusion, this collaborative filtering recommendation system will help user in decision making. Further research on other approaches in implementing recommender system in tourism domain can help in information delivery.