[
  {
    "id": "10.1007/978-981-15-7394-1_7",
    "type": "paper-conference",
    "title": "Novel User Preference Recommender System Based on Twitter Profile Analysis",
    "author": [
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      },
      {
        "family": "Rakshit",
        "given": "Sandip"
      },
      {
        "family": "Oshogbunu",
        "given": "Michael"
      },
      {
        "family": "Salisu",
        "given": "Shafiu"
      }
    ],
    "editor": [
      {
        "literal": "Samarjeet Borah"
      },
      {
        "literal": "Ratika Pradhan"
      },
      {
        "literal": "Nilanjan Dey"
      },
      {
        "literal": "Phalguni Gupta"
      }
    ],
    "container-title": "Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020)",
    "collection-title": "Advances in Intelligent Systems and Computing",
    "issued": {
      "date-parts": [
        [
          2020,
          11,
          28
        ]
      ]
    },
    "volume": "1248",
    "page": "85-93",
    "publisher": "Springer Singapore",
    "ISSN": "2194-5357",
    "ISBN": "9789811573941",
    "DOI": "10.1007/978-981-15-7394-1_7",
    "URL": "https://www.narasimharao.net/research/twitter-profile-recommender-system-ibm-watson/",
    "abstract": "Recommender systems can help provide preference-based personalized services to consumers and help them make informed decisions. However, a key shortcoming of the recommender systems is the lack of interactive methods to dynamically change the weights of recommendation algorithms. Our proposed system uses the Twitter profile and tweets to identify the interests of a user and then recommends the relevant products and services to that user. Our recommendation system is built to predict and personalize products and services based on the result of mining and analyzing the user’s Twitter timeline. The proposed recommender system is built upon an artificial intelligence platform called IBM Watson. The experimental result from the platform displayed the category of goods and services the user is most likely to consume. Our recommender system also showed a strong correlation between the category of products and services a user consumes and his/her tweets.",
    "keyword": "Personalization, Collaborative, Recommender, E-commerce, Twitter, Mining, Hybrid, Retrieval, recommender systems, personalisation, Twitter data mining, social media analytics, user profiling, IBM Watson, e-commerce, hybrid recommendation, text mining, consumer preference prediction",
    "language": "en"
  }
]