7. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, receive data specifying a reference point-of-interest specified by a user and location data of a search region; retrieve a reference vector specifying a plurality of features associated with the reference point-of-interest; determine a plurality of candidates for similar points-of-interest based, at least in part, on the search region; retrieve candidate feature vectors specifying a plurality of features associated with respective candidates by at least real-time extracting semantic topics from one or more text descriptions and one or more user reviews for each of the candidates using a language model assigned a probability to each word thereof, and generating entries of the semantic topics in the feature vectors for each of the candidates, wherein the user reviews include one or more reviews by the user; determine a similarity score for each of the candidates via comparing the plurality of features of respective candidates to the plurality of features of the reference point-of-interest to determine a number of common features (n) shared between the plurality of features of the respective candidates and the plurality of features of the reference point-of-interest, and calculating the similarity score using an equation including a weighting vector (w), the reference feature vector (r), a respective candidate feature vector (p): similarity ⁢ ⁢ ( r , p ) = ∑ i = 1 -> n ⁢ w i ⁢ r i ⁢ p i where i=1 to the number of common features shared between the reference vector and the candidate feature vectors; and generate a list of one or more similar points-of-interest based on the similarity scores.
7. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, receive data specifying a reference point-of-interest specified by a user and location data of a search region; retrieve a reference vector specifying a plurality of features associated with the reference point-of-interest; determine a plurality of candidates for similar points-of-interest based, at least in part, on the search region; retrieve candidate feature vectors specifying a plurality of features associated with respective candidates by at least real-time extracting semantic topics from one or more text descriptions and one or more user reviews for each of the candidates using a language model assigned a probability to each word thereof, and generating entries of the semantic topics in the feature vectors for each of the candidates, wherein the user reviews include one or more reviews by the user; determine a similarity score for each of the candidates via comparing the plurality of features of respective candidates to the plurality of features of the reference point-of-interest to determine a number of common features (n) shared between the plurality of features of the respective candidates and the plurality of features of the reference point-of-interest, and calculating the similarity score using an equation including a weighting vector (w), the reference feature vector (r), a respective candidate feature vector (p): similarity ⁢ ⁢ ( r , p ) = ∑ i = 1 -> n ⁢ w i ⁢ r i ⁢ p i where i=1 to the number of common features shared between the reference vector and the candidate feature vectors; and generate a list of one or more similar points-of-interest based on the similarity scores. 11. The apparatus of claim 7 , wherein the data is received from a user equipment and the search region is determined based on a location of the user equipment.