Model-Based Location Recommender System Using Geotagged Photos On Instagram

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Memarzadeh Maryam; KAMANDI ALI

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Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2020 | Issue:6

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Title

Model-Based Location Recommender System Using Geotagged Photos On Instagram

Author(s)

Memarzadeh Maryam | KAMANDI ALI

Keywords

location-based social networks

geo-tagged media

recommender systems

FastText

Abstract

Instagram is one of the popular social media services used by a variety of people around the world. It has a huge number of active users. The more users, the larger and the more different Instagram data are available. In this paper, we propose a Model-based location recommender system (MLRS), which creates a profile for each location and uses it to recommend locations, based on user interests. Since our analysis does not have an appropriate dataset to check, we use both Foursquare and Instagram to create our dataset. Next, we propose the Term-Frequency and Inverse Document Frequency(TF-IDF) method to rank extracted hashtags of selected Instagram locations based on Instagram image captions. This gives us the main idea of locations, based on 30 recent image captions hashtag posted. Then, we used FastText to classify hashtags of each location post. We evaluated our system with a large-scale real dataset collected from Instagram concerning precision, recall and the F-measure. Finally, the experimental results show that the highest result achieved when the FastText model tested with n=1 with an F-measure of 77. 8%.

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APA: Copy

Memarzadeh, Maryam, & KAMANDI, ALI. (2020). Model-Based Location Recommender System Using Geotagged Photos On Instagram. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949249/en

Vancouver: Copy

Memarzadeh Maryam, KAMANDI ALI. Model-Based Location Recommender System Using Geotagged Photos On Instagram. 2020. Available from: https://sid.ir/paper/949249/en

IEEE: Copy

Maryam Memarzadeh, and ALI KAMANDI, “Model-Based Location Recommender System Using Geotagged Photos On Instagram,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949249/en

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