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@article{145702,
        author = {PIDUGU MOHAN RAO and A MALLIKARJUNA and Prof. S. RAMAKRISHNA},
        title = {A cocktail approach for Travel Package Recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {111-116},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145702},
        abstract = {Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations,. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area- travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. },
        keywords = {A cocktail approach for travel package recommendation, to providing  the list of Personalized travel package recommendation.
},
        month = {},
        }