Online shopping of grocery and gourmet products differ from other shopping activities due to its routine nature of buy-consume-buy. The existing recommendation algorithms of ecommerce websites are suitable only to render recommendation for products of one time purchase. So, in order to identify and recommend the products that users are likely to buy again and again, a novel recommender algorithm is proposed based on linguistic decision analysis model. The proposed buyagain recommender algorithm finds the semantic value of the user comments and computes the semantic value along with the user rating to render recommendation to the user. The efficiency of the buyagain recommender algorithm is evaluated using the grocery and gourmet dataset of amazon ecommerce websites. The end result proves that the algorithm accurately recommends the product that the user likes to purchase once again.