博客

该模块的主要思路是将现登录用户的个人信息（包括出行目的地、旅游类型、天数、花费等）与数据库中已有的其他用户信息进行相似度匹配，根据相似度值的大小进行排序，同时获得用户去过的景点，然后推荐给用户。这里的相似度计算采用的是余弦相似度，关于它是什么以及如何计算，我是参考的

这个博客（侵删）。

里面是对一段文本先进行分词，但是该模块用到的数据都是像目的地、天数之类的，因此不需要进行分词操作。最后的效果如下：

核心代码：

RecommnedFragment

public class RecommendFragment extends Fragment {

    private String mCurrentUserName=UserCenterFragment.mUserName;   //当前所登陆的用户,一定要保证已经登录，否则可能有问题
    private UserLab mUserLab;
    private static List<SimilarityItem> mSimilarityItems=new ArrayList<>();

    private RecyclerView mRecyclerView;

    public static RecommendFragment newInstance(){
        return new RecommendFragment();
    }

    @Override
    public View onCreateView(LayoutInflater inflater,  ViewGroup container, Bundle savedInstanceState) {
        View v=inflater.inflate(R.layout.fragment_recommend,container,false);
        mRecyclerView=(RecyclerView)v.findViewById(R.id.recommend_recycler_view);
        mRecyclerView.setLayoutManager(new LinearLayoutManager(getActivity()));
        getSimiliarity(mSimilarityItems);  // 先准备数据，并进行排序
        Collections.sort(mSimilarityItems, new Comparator<SimilarityItem>() {
            @Override
            public int compare(SimilarityItem similarityItem, SimilarityItem t1) {
                return (int) ((t1.getSimilarity()-similarityItem.getSimilarity())*100);//由于这里返回值必须是int型，而相似度都是小数，故先放大100倍
            }
        });
        int rank=1;
        for(SimilarityItem item:mSimilarityItems){
            item.setRank(rank);
            rank++;
        }
        setupAdapter();
        return v;
    }

    private class RecommendHolder extends RecyclerView.ViewHolder{
        private TextView mRecommendRank;
        private TextView mRecommendSimilarity;
        private TextView mRecommendScenery;

        public RecommendHolder(View itemView){
            super(itemView);
            mRecommendRank=(TextView)itemView.findViewById(R.id.recommend_rank);
            mRecommendSimilarity=(TextView)itemView.findViewById(R.id.recommend_similarity);
            mRecommendScenery=(TextView)itemView.findViewById(R.id.recommend_scenery);
        }

        public void bindItem(SimilarityItem  item){
            mRecommendRank.setText(item.getRank()+"");
            mRecommendSimilarity.setText("相似度："+item.getSimilarity());
            mRecommendScenery.setText(item.getScenery());
        }
    }

    private class RecommendAdapter extends RecyclerView.Adapter<RecommendHolder>{
        private List<SimilarityItem> mItems;

        public RecommendAdapter(List<SimilarityItem> items){
            mItems=items;
        }

        @Override
        public RecommendHolder onCreateViewHolder(ViewGroup parent, int viewType) {
            View view =LayoutInflater.from(getActivity()).inflate(R.layout.list_item_recommend,parent,false);
            return new RecommendHolder(view);
        }

        @Override
        public void onBindViewHolder(RecommendHolder holder, int position) {
            holder.bindItem(mItems.get(position));
        }

        @Override
        public int getItemCount() {
            return mItems.size();
        }
    }

    public void setupAdapter(){
        mRecyclerView.setAdapter(new RecommendAdapter(mSimilarityItems));
    }

    /**
     * 得到每条记录的相似度及对应景点，存储在Sinilarity类中最后存在容器里
     * @param similarityItems
     */
    private void getSimiliarity(List<SimilarityItem> similarityItems){
        mUserLab=UserLab.get(getActivity());
        List<String> words=mUserLab.getUserWords(mCurrentUserName);
        List<Words> wordsArray=mUserLab.getTripWordsArray();
        CosineSimilarity cosineSimilarity = new CosineSimilarity();

        for (Words w:wordsArray){
            List<String> words1=w.getWordArray();
            SimilarityItem item=new SimilarityItem();
            item.setSimilarity(cosineSimilarity.cos(words, words1));
            item.setScenery(w.getScenery());
            similarityItems.add(item);
        }

    }

    /**
     * 得到推荐相似度最高的景点数组
     * @return
     */
    public static String[] getRecommendSceneries(){
        String sceneries=mSimilarityItems.get(0).getScenery();  //获取相似度最高的，也就是排序后的第一个,这里得到的是字符串
        sceneries.trim();
        String[] recommendSceneries=sceneries.split("，");
        return recommendSceneries;
    }
}

整个工

程的源码在这找！